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@virtual def on_tick(self, tick: TickData): '\n Callback of new tick data update.\n ' pass
-5,404,603,894,278,310,000
Callback of new tick data update.
vnpy/app/cta_strategy_pro/template.py
on_tick
UtorYeung/vnpy
python
@virtual def on_tick(self, tick: TickData): '\n \n ' pass
@virtual def on_bar(self, bar: BarData): '\n Callback of new bar data update.\n ' pass
959,336,517,627,439,700
Callback of new bar data update.
vnpy/app/cta_strategy_pro/template.py
on_bar
UtorYeung/vnpy
python
@virtual def on_bar(self, bar: BarData): '\n \n ' pass
@virtual def on_trade(self, trade: TradeData): '\n Callback of new trade data update.\n ' pass
-2,060,187,208,861,163,800
Callback of new trade data update.
vnpy/app/cta_strategy_pro/template.py
on_trade
UtorYeung/vnpy
python
@virtual def on_trade(self, trade: TradeData): '\n \n ' pass
@virtual def on_order(self, order: OrderData): '\n Callback of new order data update.\n ' pass
685,206,543,045,577,000
Callback of new order data update.
vnpy/app/cta_strategy_pro/template.py
on_order
UtorYeung/vnpy
python
@virtual def on_order(self, order: OrderData): '\n \n ' pass
@virtual def on_stop_order(self, stop_order: StopOrder): '\n Callback of stop order update.\n ' pass
7,482,887,281,911,738,000
Callback of stop order update.
vnpy/app/cta_strategy_pro/template.py
on_stop_order
UtorYeung/vnpy
python
@virtual def on_stop_order(self, stop_order: StopOrder): '\n \n ' pass
def buy(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str='', order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n Send buy order to open a long position.\n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_upper_limit(vt_symbol): self.write_error(u'涨停价不做FAK/FOK委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.LONG, offset=Offset.OPEN, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
-9,080,480,501,627,685,000
Send buy order to open a long position.
vnpy/app/cta_strategy_pro/template.py
buy
UtorYeung/vnpy
python
def buy(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str=, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n \n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_upper_limit(vt_symbol): self.write_error(u'涨停价不做FAK/FOK委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.LONG, offset=Offset.OPEN, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
def sell(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str='', order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n Send sell order to close a long position.\n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_lower_limit(vt_symbol): self.write_error(u'跌停价不做FAK/FOK sell委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.SHORT, offset=Offset.CLOSE, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
6,360,702,185,854,788,000
Send sell order to close a long position.
vnpy/app/cta_strategy_pro/template.py
sell
UtorYeung/vnpy
python
def sell(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str=, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n \n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_lower_limit(vt_symbol): self.write_error(u'跌停价不做FAK/FOK sell委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.SHORT, offset=Offset.CLOSE, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
def short(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str='', order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n Send short order to open as short position.\n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_lower_limit(vt_symbol): self.write_error(u'跌停价不做FAK/FOK short委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.SHORT, offset=Offset.OPEN, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
4,953,141,459,008,334,000
Send short order to open as short position.
vnpy/app/cta_strategy_pro/template.py
short
UtorYeung/vnpy
python
def short(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str=, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n \n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_lower_limit(vt_symbol): self.write_error(u'跌停价不做FAK/FOK short委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.SHORT, offset=Offset.OPEN, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
def cover(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str='', order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n Send cover order to close a short position.\n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_upper_limit(vt_symbol): self.write_error(u'涨停价不做FAK/FOK cover委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.LONG, offset=Offset.CLOSE, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
6,435,601,325,156,773,000
Send cover order to close a short position.
vnpy/app/cta_strategy_pro/template.py
cover
UtorYeung/vnpy
python
def cover(self, price: float, volume: float, stop: bool=False, lock: bool=False, vt_symbol: str=, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n \n ' if (order_type in [OrderType.FAK, OrderType.FOK]): if self.is_upper_limit(vt_symbol): self.write_error(u'涨停价不做FAK/FOK cover委托') return [] if (volume == 0): self.write_error(f'委托数量有误,必须大于0,{vt_symbol}, price:{price}') return [] return self.send_order(vt_symbol=vt_symbol, direction=Direction.LONG, offset=Offset.CLOSE, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type, order_time=order_time, grid=grid)
def send_order(self, vt_symbol: str, direction: Direction, offset: Offset, price: float, volume: float, stop: bool=False, lock: bool=False, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n Send a new order.\n ' if (vt_symbol == ''): vt_symbol = self.vt_symbol if (not self.trading): self.write_log(f'非交易状态') return [] vt_orderids = self.cta_engine.send_order(strategy=self, vt_symbol=vt_symbol, direction=direction, offset=offset, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type) if (len(vt_orderids) == 0): self.write_error(f'{self.strategy_name}调用cta_engine.send_order委托返回失败,vt_symbol:{vt_symbol}') if (order_time is None): order_time = datetime.now() for vt_orderid in vt_orderids: d = {'direction': direction, 'offset': offset, 'vt_symbol': vt_symbol, 'price': price, 'volume': volume, 'order_type': order_type, 'traded': 0, 'order_time': order_time, 'status': Status.SUBMITTING} if grid: d.update({'grid': grid}) grid.order_ids.append(vt_orderid) self.active_orders.update({vt_orderid: d}) if (direction == Direction.LONG): self.entrust = 1 elif (direction == Direction.SHORT): self.entrust = (- 1) return vt_orderids
-7,242,398,835,969,134,000
Send a new order.
vnpy/app/cta_strategy_pro/template.py
send_order
UtorYeung/vnpy
python
def send_order(self, vt_symbol: str, direction: Direction, offset: Offset, price: float, volume: float, stop: bool=False, lock: bool=False, order_type: OrderType=OrderType.LIMIT, order_time: datetime=None, grid: CtaGrid=None): '\n \n ' if (vt_symbol == ): vt_symbol = self.vt_symbol if (not self.trading): self.write_log(f'非交易状态') return [] vt_orderids = self.cta_engine.send_order(strategy=self, vt_symbol=vt_symbol, direction=direction, offset=offset, price=price, volume=volume, stop=stop, lock=lock, order_type=order_type) if (len(vt_orderids) == 0): self.write_error(f'{self.strategy_name}调用cta_engine.send_order委托返回失败,vt_symbol:{vt_symbol}') if (order_time is None): order_time = datetime.now() for vt_orderid in vt_orderids: d = {'direction': direction, 'offset': offset, 'vt_symbol': vt_symbol, 'price': price, 'volume': volume, 'order_type': order_type, 'traded': 0, 'order_time': order_time, 'status': Status.SUBMITTING} if grid: d.update({'grid': grid}) grid.order_ids.append(vt_orderid) self.active_orders.update({vt_orderid: d}) if (direction == Direction.LONG): self.entrust = 1 elif (direction == Direction.SHORT): self.entrust = (- 1) return vt_orderids
def cancel_order(self, vt_orderid: str): '\n Cancel an existing order.\n ' if self.trading: return self.cta_engine.cancel_order(self, vt_orderid) return False
6,330,077,215,582,117,000
Cancel an existing order.
vnpy/app/cta_strategy_pro/template.py
cancel_order
UtorYeung/vnpy
python
def cancel_order(self, vt_orderid: str): '\n \n ' if self.trading: return self.cta_engine.cancel_order(self, vt_orderid) return False
def cancel_all(self): '\n Cancel all orders sent by strategy.\n ' if self.trading: self.cta_engine.cancel_all(self)
4,049,518,702,072,619,000
Cancel all orders sent by strategy.
vnpy/app/cta_strategy_pro/template.py
cancel_all
UtorYeung/vnpy
python
def cancel_all(self): '\n \n ' if self.trading: self.cta_engine.cancel_all(self)
def is_upper_limit(self, symbol): '是否涨停' tick = self.tick_dict.get(symbol, None) if ((tick is None) or (tick.limit_up is None) or (tick.limit_up == 0)): return False if (tick.bid_price_1 == tick.limit_up): return True
-3,416,308,772,781,506,000
是否涨停
vnpy/app/cta_strategy_pro/template.py
is_upper_limit
UtorYeung/vnpy
python
def is_upper_limit(self, symbol): tick = self.tick_dict.get(symbol, None) if ((tick is None) or (tick.limit_up is None) or (tick.limit_up == 0)): return False if (tick.bid_price_1 == tick.limit_up): return True
def is_lower_limit(self, symbol): '是否跌停' tick = self.tick_dict.get(symbol, None) if ((tick is None) or (tick.limit_down is None) or (tick.limit_down == 0)): return False if (tick.ask_price_1 == tick.limit_down): return True
2,973,849,828,281,988,000
是否跌停
vnpy/app/cta_strategy_pro/template.py
is_lower_limit
UtorYeung/vnpy
python
def is_lower_limit(self, symbol): tick = self.tick_dict.get(symbol, None) if ((tick is None) or (tick.limit_down is None) or (tick.limit_down == 0)): return False if (tick.ask_price_1 == tick.limit_down): return True
def write_log(self, msg: str, level: int=INFO): '\n Write a log message.\n ' self.cta_engine.write_log(msg=msg, strategy_name=self.strategy_name, level=level)
-3,863,003,474,144,610,000
Write a log message.
vnpy/app/cta_strategy_pro/template.py
write_log
UtorYeung/vnpy
python
def write_log(self, msg: str, level: int=INFO): '\n \n ' self.cta_engine.write_log(msg=msg, strategy_name=self.strategy_name, level=level)
def write_error(self, msg: str): 'write error log message' self.write_log(msg=msg, level=ERROR)
3,193,733,022,767,435,000
write error log message
vnpy/app/cta_strategy_pro/template.py
write_error
UtorYeung/vnpy
python
def write_error(self, msg: str): self.write_log(msg=msg, level=ERROR)
def get_engine_type(self): '\n Return whether the cta_engine is backtesting or live trading.\n ' return self.cta_engine.get_engine_type()
7,297,224,918,648,383,000
Return whether the cta_engine is backtesting or live trading.
vnpy/app/cta_strategy_pro/template.py
get_engine_type
UtorYeung/vnpy
python
def get_engine_type(self): '\n \n ' return self.cta_engine.get_engine_type()
def load_bar(self, days: int, interval: Interval=Interval.MINUTE, callback: Callable=None, interval_num: int=1): '\n Load historical bar data for initializing strategy.\n ' if (not callback): callback = self.on_bar self.cta_engine.load_bar(self.vt_symbol, days, interval, callback, interval_num)
294,891,323,061,072,960
Load historical bar data for initializing strategy.
vnpy/app/cta_strategy_pro/template.py
load_bar
UtorYeung/vnpy
python
def load_bar(self, days: int, interval: Interval=Interval.MINUTE, callback: Callable=None, interval_num: int=1): '\n \n ' if (not callback): callback = self.on_bar self.cta_engine.load_bar(self.vt_symbol, days, interval, callback, interval_num)
def load_tick(self, days: int): '\n Load historical tick data for initializing strategy.\n ' self.cta_engine.load_tick(self.vt_symbol, days, self.on_tick)
5,586,702,844,267,469,000
Load historical tick data for initializing strategy.
vnpy/app/cta_strategy_pro/template.py
load_tick
UtorYeung/vnpy
python
def load_tick(self, days: int): '\n \n ' self.cta_engine.load_tick(self.vt_symbol, days, self.on_tick)
def put_event(self): '\n Put an strategy data event for ui update.\n ' if self.inited: self.cta_engine.put_strategy_event(self)
8,639,283,256,132,671,000
Put an strategy data event for ui update.
vnpy/app/cta_strategy_pro/template.py
put_event
UtorYeung/vnpy
python
def put_event(self): '\n \n ' if self.inited: self.cta_engine.put_strategy_event(self)
def send_email(self, msg): '\n Send email to default receiver.\n ' if self.inited: self.cta_engine.send_email(msg, self)
6,436,060,040,956,104,000
Send email to default receiver.
vnpy/app/cta_strategy_pro/template.py
send_email
UtorYeung/vnpy
python
def send_email(self, msg): '\n \n ' if self.inited: self.cta_engine.send_email(msg, self)
def sync_data(self): '\n Sync strategy variables value into disk storage.\n ' if self.trading: self.cta_engine.sync_strategy_data(self)
943,088,262,176,522,200
Sync strategy variables value into disk storage.
vnpy/app/cta_strategy_pro/template.py
sync_data
UtorYeung/vnpy
python
def sync_data(self): '\n \n ' if self.trading: self.cta_engine.sync_strategy_data(self)
@virtual def on_tick(self, tick: TickData): '\n Callback of new tick data update.\n ' pass
-5,404,603,894,278,310,000
Callback of new tick data update.
vnpy/app/cta_strategy_pro/template.py
on_tick
UtorYeung/vnpy
python
@virtual def on_tick(self, tick: TickData): '\n \n ' pass
@virtual def on_bar(self, bar: BarData): '\n Callback of new bar data update.\n ' pass
959,336,517,627,439,700
Callback of new bar data update.
vnpy/app/cta_strategy_pro/template.py
on_bar
UtorYeung/vnpy
python
@virtual def on_bar(self, bar: BarData): '\n \n ' pass
@virtual def on_tick(self, tick: TickData): '\n Callback of new tick data update.\n ' self.last_tick = tick if self.trading: self.trade()
319,937,858,261,153,700
Callback of new tick data update.
vnpy/app/cta_strategy_pro/template.py
on_tick
UtorYeung/vnpy
python
@virtual def on_tick(self, tick: TickData): '\n \n ' self.last_tick = tick if self.trading: self.trade()
@virtual def on_bar(self, bar: BarData): '\n Callback of new bar data update.\n ' self.last_bar = bar
3,089,873,859,974,323,000
Callback of new bar data update.
vnpy/app/cta_strategy_pro/template.py
on_bar
UtorYeung/vnpy
python
@virtual def on_bar(self, bar: BarData): '\n \n ' self.last_bar = bar
@virtual def on_order(self, order: OrderData): '\n Callback of new order data update.\n ' vt_orderid = order.vt_orderid if ((not order.is_active()) and (vt_orderid in self.vt_orderids)): self.vt_orderids.remove(vt_orderid)
2,346,660,776,233,848,300
Callback of new order data update.
vnpy/app/cta_strategy_pro/template.py
on_order
UtorYeung/vnpy
python
@virtual def on_order(self, order: OrderData): '\n \n ' vt_orderid = order.vt_orderid if ((not order.is_active()) and (vt_orderid in self.vt_orderids)): self.vt_orderids.remove(vt_orderid)
def update_setting(self, setting: dict): '\n Update strategy parameter wtih value in setting dict.\n ' for name in self.parameters: if (name in setting): setattr(self, name, setting[name]) (symbol, self.exchange) = extract_vt_symbol(self.vt_symbol) if (self.idx_symbol is None): self.idx_symbol = ((get_underlying_symbol(symbol).upper() + '99.') + self.exchange.value) if (self.vt_symbol != self.idx_symbol): self.write_log(f'指数合约:{self.idx_symbol}, 主力合约:{self.vt_symbol}') self.price_tick = self.cta_engine.get_price_tick(self.vt_symbol) self.symbol_size = self.cta_engine.get_size(self.vt_symbol) self.margin_rate = self.cta_engine.get_margin_rate(self.vt_symbol)
-4,360,711,793,639,846,000
Update strategy parameter wtih value in setting dict.
vnpy/app/cta_strategy_pro/template.py
update_setting
UtorYeung/vnpy
python
def update_setting(self, setting: dict): '\n \n ' for name in self.parameters: if (name in setting): setattr(self, name, setting[name]) (symbol, self.exchange) = extract_vt_symbol(self.vt_symbol) if (self.idx_symbol is None): self.idx_symbol = ((get_underlying_symbol(symbol).upper() + '99.') + self.exchange.value) if (self.vt_symbol != self.idx_symbol): self.write_log(f'指数合约:{self.idx_symbol}, 主力合约:{self.vt_symbol}') self.price_tick = self.cta_engine.get_price_tick(self.vt_symbol) self.symbol_size = self.cta_engine.get_size(self.vt_symbol) self.margin_rate = self.cta_engine.get_margin_rate(self.vt_symbol)
def sync_data(self): '同步更新数据' if (not self.backtesting): self.write_log(u'保存k线缓存数据') self.save_klines_to_cache() if (self.inited and self.trading): self.write_log(u'保存policy数据') self.policy.save()
-5,144,947,309,974,197,000
同步更新数据
vnpy/app/cta_strategy_pro/template.py
sync_data
UtorYeung/vnpy
python
def sync_data(self): if (not self.backtesting): self.write_log(u'保存k线缓存数据') self.save_klines_to_cache() if (self.inited and self.trading): self.write_log(u'保存policy数据') self.policy.save()
def save_klines_to_cache(self, kline_names: list=[]): '\n 保存K线数据到缓存\n :param kline_names: 一般为self.klines的keys\n :return:\n ' if (len(kline_names) == 0): kline_names = list(self.klines.keys()) save_path = self.cta_engine.get_data_path() file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_klines.pkb2')) with bz2.BZ2File(file_name, 'wb') as f: klines = {} for kline_name in kline_names: kline = self.klines.get(kline_name, None) klines.update({kline_name: kline}) pickle.dump(klines, f)
1,396,282,516,568,408,800
保存K线数据到缓存 :param kline_names: 一般为self.klines的keys :return:
vnpy/app/cta_strategy_pro/template.py
save_klines_to_cache
UtorYeung/vnpy
python
def save_klines_to_cache(self, kline_names: list=[]): '\n 保存K线数据到缓存\n :param kline_names: 一般为self.klines的keys\n :return:\n ' if (len(kline_names) == 0): kline_names = list(self.klines.keys()) save_path = self.cta_engine.get_data_path() file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_klines.pkb2')) with bz2.BZ2File(file_name, 'wb') as f: klines = {} for kline_name in kline_names: kline = self.klines.get(kline_name, None) klines.update({kline_name: kline}) pickle.dump(klines, f)
def load_klines_from_cache(self, kline_names: list=[]): '\n 从缓存加载K线数据\n :param kline_names:\n :return:\n ' if (len(kline_names) == 0): kline_names = list(self.klines.keys()) save_path = self.cta_engine.get_data_path() file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_klines.pkb2')) try: last_bar_dt = None with bz2.BZ2File(file_name, 'rb') as f: klines = pickle.load(f) for kline_name in kline_names: cache_kline = klines.get(kline_name, None) strategy_kline = self.klines.get(kline_name, None) if (cache_kline and strategy_kline): cb_on_bar = strategy_kline.cb_on_bar strategy_kline.__dict__.update(cache_kline.__dict__) kline_first_bar_dt = None kline_last_bar_dt = None if (len(strategy_kline.line_bar) > 0): kline_first_bar_dt = strategy_kline.line_bar[0].datetime kline_last_bar_dt = strategy_kline.line_bar[(- 1)].datetime if (last_bar_dt and strategy_kline.cur_datetime): last_bar_dt = max(last_bar_dt, strategy_kline.cur_datetime) else: last_bar_dt = strategy_kline.cur_datetime strategy_kline.strategy = self strategy_kline.cb_on_bar = cb_on_bar self.write_log(f'恢复{kline_name}缓存数据:[{kline_first_bar_dt}] => [{kline_last_bar_dt}], bar结束时间:{last_bar_dt}') self.write_log(u'加载缓存k线数据完毕') return last_bar_dt except Exception as ex: self.write_error(f'加载缓存K线数据失败:{str(ex)}') return None
-1,286,653,908,774,571,500
从缓存加载K线数据 :param kline_names: :return:
vnpy/app/cta_strategy_pro/template.py
load_klines_from_cache
UtorYeung/vnpy
python
def load_klines_from_cache(self, kline_names: list=[]): '\n 从缓存加载K线数据\n :param kline_names:\n :return:\n ' if (len(kline_names) == 0): kline_names = list(self.klines.keys()) save_path = self.cta_engine.get_data_path() file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_klines.pkb2')) try: last_bar_dt = None with bz2.BZ2File(file_name, 'rb') as f: klines = pickle.load(f) for kline_name in kline_names: cache_kline = klines.get(kline_name, None) strategy_kline = self.klines.get(kline_name, None) if (cache_kline and strategy_kline): cb_on_bar = strategy_kline.cb_on_bar strategy_kline.__dict__.update(cache_kline.__dict__) kline_first_bar_dt = None kline_last_bar_dt = None if (len(strategy_kline.line_bar) > 0): kline_first_bar_dt = strategy_kline.line_bar[0].datetime kline_last_bar_dt = strategy_kline.line_bar[(- 1)].datetime if (last_bar_dt and strategy_kline.cur_datetime): last_bar_dt = max(last_bar_dt, strategy_kline.cur_datetime) else: last_bar_dt = strategy_kline.cur_datetime strategy_kline.strategy = self strategy_kline.cb_on_bar = cb_on_bar self.write_log(f'恢复{kline_name}缓存数据:[{kline_first_bar_dt}] => [{kline_last_bar_dt}], bar结束时间:{last_bar_dt}') self.write_log(u'加载缓存k线数据完毕') return last_bar_dt except Exception as ex: self.write_error(f'加载缓存K线数据失败:{str(ex)}') return None
def get_klines_snapshot(self): '返回当前klines的切片数据' try: d = {'strategy': self.strategy_name, 'datetime': datetime.now()} klines = {} for kline_name in sorted(self.klines.keys()): klines.update({kline_name: self.klines.get(kline_name).get_data()}) kline_names = list(klines.keys()) binary_data = zlib.compress(pickle.dumps(klines)) d.update({'kline_names': kline_names, 'klines': binary_data, 'zlib': True}) return d except Exception as ex: self.write_error(f'获取klines切片数据失败:{str(ex)}') return {}
-1,498,021,857,701,842,700
返回当前klines的切片数据
vnpy/app/cta_strategy_pro/template.py
get_klines_snapshot
UtorYeung/vnpy
python
def get_klines_snapshot(self): try: d = {'strategy': self.strategy_name, 'datetime': datetime.now()} klines = {} for kline_name in sorted(self.klines.keys()): klines.update({kline_name: self.klines.get(kline_name).get_data()}) kline_names = list(klines.keys()) binary_data = zlib.compress(pickle.dumps(klines)) d.update({'kline_names': kline_names, 'klines': binary_data, 'zlib': True}) return d except Exception as ex: self.write_error(f'获取klines切片数据失败:{str(ex)}') return {}
def init_position(self): '\n 初始化Positin\n 使用网格的持久化,获取开仓状态的多空单,更新\n :return:\n ' self.write_log(u'init_position(),初始化持仓') pos_symbols = set() remove_ids = [] if (len(self.gt.up_grids) <= 0): self.position.short_pos = 0 short_grids = self.gt.load(direction=Direction.SHORT, open_status_filter=[True]) if (len(short_grids) == 0): self.write_log(u'没有持久化的空单数据') self.gt.up_grids = [] else: self.gt.up_grids = short_grids for sg in short_grids: if ((len(sg.order_ids) > 0) or sg.order_status): self.write_log(f'重置委托状态:{sg.order_status},清除委托单:{sg.order_ids}') sg.order_status = False sg.order_ids = [] short_symbol = sg.snapshot.get('mi_symbol', self.vt_symbol) if (sg.traded_volume > 0): if (sg.open_status and (sg.volume == sg.traded_volume)): msg = f'{self.strategy_name} {short_symbol}空单持仓{sg.volume},已成交:{sg.traded_volume},不加载' self.write_log(msg) self.send_wechat(msg) remove_ids.append(sg.id) continue pos_symbols.add(short_symbol) self.write_log(u'加载持仓空单[ID:{},vt_symbol:{},价格:{}],[指数:{},价格:{}],数量:{}手'.format(sg.id, short_symbol, sg.snapshot.get('open_price'), self.idx_symbol, sg.open_price, sg.volume)) self.position.short_pos -= sg.volume self.write_log(u'持久化空单,共持仓:{}手'.format(abs(self.position.short_pos))) if (len(remove_ids) > 0): self.gt.remove_grids_by_ids(direction=Direction.SHORT, ids=remove_ids) remove_ids = [] if (len(self.gt.dn_grids) <= 0): self.position.long_pos = 0 long_grids = self.gt.load(direction=Direction.LONG, open_status_filter=[True]) if (len(long_grids) == 0): self.write_log(u'没有持久化的多单数据') self.gt.dn_grids = [] else: self.gt.dn_grids = long_grids for lg in long_grids: if ((len(lg.order_ids) > 0) or lg.order_status): self.write_log(f'重置委托状态:{lg.order_status},清除委托单:{lg.order_ids}') lg.order_status = False lg.order_ids = [] long_symbol = lg.snapshot.get('mi_symbol', self.vt_symbol) if (lg.traded_volume > 0): if (lg.open_status and (lg.volume == lg.traded_volume)): msg = f'{self.strategy_name} {long_symbol}多单持仓{lg.volume},已成交:{lg.traded_volume},不加载' self.write_log(msg) self.send_wechat(msg) remove_ids.append(lg.id) continue pos_symbols.add(long_symbol) self.write_log(u'加载持仓多单[ID:{},vt_symbol:{},价格:{}],[指数{},价格:{}],数量:{}手'.format(lg.id, long_symbol, lg.snapshot.get('open_price'), self.idx_symbol, lg.open_price, lg.volume)) self.position.long_pos += lg.volume self.write_log(f'持久化多单,共持仓:{self.position.long_pos}手') if (len(remove_ids) > 0): self.gt.remove_grids_by_ids(direction=Direction.LONG, ids=remove_ids) self.position.pos = (self.position.long_pos + self.position.short_pos) self.write_log(u'{}加载持久化数据完成,多单:{},空单:{},共:{}手'.format(self.strategy_name, self.position.long_pos, abs(self.position.short_pos), self.position.pos)) self.pos = self.position.pos self.gt.save() self.display_grids() if ((len(self.vt_symbol) > 0) and (self.vt_symbol not in pos_symbols)): pos_symbols.add(self.vt_symbol) if (self.idx_symbol and (self.idx_symbol not in pos_symbols)): pos_symbols.add(self.idx_symbol) for symbol in list(pos_symbols): self.write_log(f'新增订阅合约:{symbol}') self.cta_engine.subscribe_symbol(strategy_name=self.strategy_name, vt_symbol=symbol)
-3,441,370,031,846,856,000
初始化Positin 使用网格的持久化,获取开仓状态的多空单,更新 :return:
vnpy/app/cta_strategy_pro/template.py
init_position
UtorYeung/vnpy
python
def init_position(self): '\n 初始化Positin\n 使用网格的持久化,获取开仓状态的多空单,更新\n :return:\n ' self.write_log(u'init_position(),初始化持仓') pos_symbols = set() remove_ids = [] if (len(self.gt.up_grids) <= 0): self.position.short_pos = 0 short_grids = self.gt.load(direction=Direction.SHORT, open_status_filter=[True]) if (len(short_grids) == 0): self.write_log(u'没有持久化的空单数据') self.gt.up_grids = [] else: self.gt.up_grids = short_grids for sg in short_grids: if ((len(sg.order_ids) > 0) or sg.order_status): self.write_log(f'重置委托状态:{sg.order_status},清除委托单:{sg.order_ids}') sg.order_status = False sg.order_ids = [] short_symbol = sg.snapshot.get('mi_symbol', self.vt_symbol) if (sg.traded_volume > 0): if (sg.open_status and (sg.volume == sg.traded_volume)): msg = f'{self.strategy_name} {short_symbol}空单持仓{sg.volume},已成交:{sg.traded_volume},不加载' self.write_log(msg) self.send_wechat(msg) remove_ids.append(sg.id) continue pos_symbols.add(short_symbol) self.write_log(u'加载持仓空单[ID:{},vt_symbol:{},价格:{}],[指数:{},价格:{}],数量:{}手'.format(sg.id, short_symbol, sg.snapshot.get('open_price'), self.idx_symbol, sg.open_price, sg.volume)) self.position.short_pos -= sg.volume self.write_log(u'持久化空单,共持仓:{}手'.format(abs(self.position.short_pos))) if (len(remove_ids) > 0): self.gt.remove_grids_by_ids(direction=Direction.SHORT, ids=remove_ids) remove_ids = [] if (len(self.gt.dn_grids) <= 0): self.position.long_pos = 0 long_grids = self.gt.load(direction=Direction.LONG, open_status_filter=[True]) if (len(long_grids) == 0): self.write_log(u'没有持久化的多单数据') self.gt.dn_grids = [] else: self.gt.dn_grids = long_grids for lg in long_grids: if ((len(lg.order_ids) > 0) or lg.order_status): self.write_log(f'重置委托状态:{lg.order_status},清除委托单:{lg.order_ids}') lg.order_status = False lg.order_ids = [] long_symbol = lg.snapshot.get('mi_symbol', self.vt_symbol) if (lg.traded_volume > 0): if (lg.open_status and (lg.volume == lg.traded_volume)): msg = f'{self.strategy_name} {long_symbol}多单持仓{lg.volume},已成交:{lg.traded_volume},不加载' self.write_log(msg) self.send_wechat(msg) remove_ids.append(lg.id) continue pos_symbols.add(long_symbol) self.write_log(u'加载持仓多单[ID:{},vt_symbol:{},价格:{}],[指数{},价格:{}],数量:{}手'.format(lg.id, long_symbol, lg.snapshot.get('open_price'), self.idx_symbol, lg.open_price, lg.volume)) self.position.long_pos += lg.volume self.write_log(f'持久化多单,共持仓:{self.position.long_pos}手') if (len(remove_ids) > 0): self.gt.remove_grids_by_ids(direction=Direction.LONG, ids=remove_ids) self.position.pos = (self.position.long_pos + self.position.short_pos) self.write_log(u'{}加载持久化数据完成,多单:{},空单:{},共:{}手'.format(self.strategy_name, self.position.long_pos, abs(self.position.short_pos), self.position.pos)) self.pos = self.position.pos self.gt.save() self.display_grids() if ((len(self.vt_symbol) > 0) and (self.vt_symbol not in pos_symbols)): pos_symbols.add(self.vt_symbol) if (self.idx_symbol and (self.idx_symbol not in pos_symbols)): pos_symbols.add(self.idx_symbol) for symbol in list(pos_symbols): self.write_log(f'新增订阅合约:{symbol}') self.cta_engine.subscribe_symbol(strategy_name=self.strategy_name, vt_symbol=symbol)
def get_positions(self): "\n 获取策略当前持仓(重构,使用主力合约)\n :return: [{'vt_symbol':symbol,'direction':direction,'volume':volume]\n " if (not self.position): return [] pos_list = [] if (self.position.long_pos > 0): for g in self.gt.get_opened_grids(direction=Direction.LONG): vt_symbol = g.snapshot.get('mi_symbol', (g.vt_symbol if (g.vt_symbol and ('99' not in g.vt_symbol)) else self.vt_symbol)) open_price = g.snapshot.get('open_price', g.open_price) pos_list.append({'vt_symbol': vt_symbol, 'direction': 'long', 'volume': (g.volume - g.traded_volume), 'price': open_price}) if (abs(self.position.short_pos) > 0): for g in self.gt.get_opened_grids(direction=Direction.SHORT): vt_symbol = g.snapshot.get('mi_symbol', (g.vt_symbol if (g.vt_symbol and ('99' not in g.vt_symbol)) else self.vt_symbol)) open_price = g.snapshot.get('open_price', g.open_price) pos_list.append({'vt_symbol': vt_symbol, 'direction': 'short', 'volume': abs((g.volume - g.traded_volume)), 'price': open_price}) if (self.cur_datetime and ((datetime.now() - self.cur_datetime).total_seconds() < 10)): self.write_log(u'当前持仓:{}'.format(pos_list)) return pos_list
-4,157,647,331,058,156,000
获取策略当前持仓(重构,使用主力合约) :return: [{'vt_symbol':symbol,'direction':direction,'volume':volume]
vnpy/app/cta_strategy_pro/template.py
get_positions
UtorYeung/vnpy
python
def get_positions(self): "\n 获取策略当前持仓(重构,使用主力合约)\n :return: [{'vt_symbol':symbol,'direction':direction,'volume':volume]\n " if (not self.position): return [] pos_list = [] if (self.position.long_pos > 0): for g in self.gt.get_opened_grids(direction=Direction.LONG): vt_symbol = g.snapshot.get('mi_symbol', (g.vt_symbol if (g.vt_symbol and ('99' not in g.vt_symbol)) else self.vt_symbol)) open_price = g.snapshot.get('open_price', g.open_price) pos_list.append({'vt_symbol': vt_symbol, 'direction': 'long', 'volume': (g.volume - g.traded_volume), 'price': open_price}) if (abs(self.position.short_pos) > 0): for g in self.gt.get_opened_grids(direction=Direction.SHORT): vt_symbol = g.snapshot.get('mi_symbol', (g.vt_symbol if (g.vt_symbol and ('99' not in g.vt_symbol)) else self.vt_symbol)) open_price = g.snapshot.get('open_price', g.open_price) pos_list.append({'vt_symbol': vt_symbol, 'direction': 'short', 'volume': abs((g.volume - g.traded_volume)), 'price': open_price}) if (self.cur_datetime and ((datetime.now() - self.cur_datetime).total_seconds() < 10)): self.write_log(u'当前持仓:{}'.format(pos_list)) return pos_list
def get_policy_json(self): '获取policy的json格式数据' if (not self.policy): return None data = self.policy.to_json() return data
-7,284,977,415,092,988,000
获取policy的json格式数据
vnpy/app/cta_strategy_pro/template.py
get_policy_json
UtorYeung/vnpy
python
def get_policy_json(self): if (not self.policy): return None data = self.policy.to_json() return data
def get_grid_trade_json(self): '获取gt组件的json格式数据' if (not self.gt): return None data = self.gt.to_json() return data
1,568,559,070,898,897,000
获取gt组件的json格式数据
vnpy/app/cta_strategy_pro/template.py
get_grid_trade_json
UtorYeung/vnpy
python
def get_grid_trade_json(self): if (not self.gt): return None data = self.gt.to_json() return data
def tns_cancel_logic(self, dt, force=False): '撤单逻辑' if (len(self.active_orders) < 1): self.entrust = 0 return for vt_orderid in list(self.active_orders.keys()): order_info = self.active_orders.get(vt_orderid) order_grid = order_info.get('grid', None) if (order_info.get('status', None) in [Status.CANCELLED, Status.REJECTED]): self.active_orders.pop(vt_orderid, None) continue order_time = order_info.get('order_time') over_ms = (dt - order_time).total_seconds() if (f'{dt.hour}:{dt.minute}' in ['10:30', '13:30']): continue if ((over_ms > self.cancel_seconds) or force): self.write_log(f'{dt}, 超时{over_ms}秒未成交,取消委托单:{order_info}') if self.cancel_order(vt_orderid): order_info.update({'status': Status.CANCELLING}) else: order_info.update({'status': Status.CANCELLED}) if order_grid: if (vt_orderid in order_grid.order_ids): order_grid.order_ids.remove(vt_orderid) if (len(order_grid.order_ids) == 0): order_grid.order_status = False if (len(self.active_orders) < 1): self.entrust = 0
-7,555,482,025,060,726,000
撤单逻辑
vnpy/app/cta_strategy_pro/template.py
tns_cancel_logic
UtorYeung/vnpy
python
def tns_cancel_logic(self, dt, force=False): if (len(self.active_orders) < 1): self.entrust = 0 return for vt_orderid in list(self.active_orders.keys()): order_info = self.active_orders.get(vt_orderid) order_grid = order_info.get('grid', None) if (order_info.get('status', None) in [Status.CANCELLED, Status.REJECTED]): self.active_orders.pop(vt_orderid, None) continue order_time = order_info.get('order_time') over_ms = (dt - order_time).total_seconds() if (f'{dt.hour}:{dt.minute}' in ['10:30', '13:30']): continue if ((over_ms > self.cancel_seconds) or force): self.write_log(f'{dt}, 超时{over_ms}秒未成交,取消委托单:{order_info}') if self.cancel_order(vt_orderid): order_info.update({'status': Status.CANCELLING}) else: order_info.update({'status': Status.CANCELLED}) if order_grid: if (vt_orderid in order_grid.order_ids): order_grid.order_ids.remove(vt_orderid) if (len(order_grid.order_ids) == 0): order_grid.order_status = False if (len(self.active_orders) < 1): self.entrust = 0
def tns_switch_long_pos(self, open_new=True): '\n 切换合约,从持仓的非主力合约,切换至主力合约\n :param open_new: 是否开仓主力合约\n :return:\n ' if (self.entrust != 0): return if (self.position.long_pos == 0): return if (self.cur_mi_price == 0): return none_mi_grid = None none_mi_symbol = None self.write_log(f'持仓换月=>启动.') for g in self.gt.get_opened_grids(direction=Direction.LONG): none_mi_symbol = g.snapshot.get('mi_symbol', g.vt_symbol) if ((none_mi_symbol is None) or (none_mi_symbol == self.vt_symbol)): self.write_log(f'none_mi_symbol:{none_mi_symbol}, vt_symbol:{self.vt_symbol} 一致,不处理') continue if ((not g.open_status) or g.order_status or ((g.volume - g.traded_volume) <= 0)): self.write_log(f'开仓状态:{g.open_status}, 委托状态:{g.order_status},网格持仓:{g.volume} ,已交易数量:{g.traded_volume}, 不处理') continue none_mi_grid = g if ((g.traded_volume > 0) and ((g.volume - g.traded_volume) > 0)): g.volume -= g.traded_volume g.traded_volume = 0 break if (none_mi_grid is None): return self.write_log(f'持仓换月=>找到多单持仓:{none_mi_symbol},持仓数量:{none_mi_grid.volume}') none_mi_tick = self.tick_dict.get(none_mi_symbol) mi_tick = self.tick_dict.get(self.vt_symbol, None) if ((none_mi_tick is None) or (mi_tick is None)): return if (self.is_upper_limit(none_mi_symbol) or self.is_upper_limit(self.vt_symbol)): self.write_log(f'{none_mi_symbol} 或 {self.vt_symbol} 为涨停价,不做换仓') return none_mi_price = max(none_mi_tick.last_price, none_mi_tick.bid_price_1) grid = deepcopy(none_mi_grid) grid.id = str(uuid.uuid1()) grid.open_status = False self.write_log(f'持仓换月=>复制持仓信息{none_mi_symbol},ID:{none_mi_grid.id} => {self.vt_symbol},ID:{grid.id}') vt_orderids = self.sell(price=none_mi_price, volume=none_mi_grid.volume, vt_symbol=none_mi_symbol, order_type=self.order_type, grid=none_mi_grid) if (len(vt_orderids) > 0): self.write_log(f'持仓换月=>委托卖出非主力合约{none_mi_symbol}持仓:{none_mi_grid.volume}') if none_mi_grid.snapshot.get('switched', False): self.write_log(f'持仓换月=>已经执行过换月,不再创建新的买入操作') return none_mi_grid.snapshot.update({'switched': True}) if (not open_new): self.write_log(f'不买入新的主力合约:{self.vt_symbol},数量:{grid.volume}') self.gt.save() return grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.dn_grids.append(grid) vt_orderids = self.buy(price=(self.cur_mi_price + (5 * self.price_tick)), volume=grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, grid=grid) if (len(vt_orderids) > 0): self.write_log(u'持仓换月=>委托买入主力合约:{},价格:{},数量:{}'.format(self.vt_symbol, self.cur_mi_price, grid.volume)) else: self.write_error(f'持仓换月=>委托买入主力合约:{self.vt_symbol}失败') self.gt.save() else: self.write_error(f'持仓换月=>委托卖出非主力合约:{none_mi_symbol}失败')
-7,377,124,469,565,173,000
切换合约,从持仓的非主力合约,切换至主力合约 :param open_new: 是否开仓主力合约 :return:
vnpy/app/cta_strategy_pro/template.py
tns_switch_long_pos
UtorYeung/vnpy
python
def tns_switch_long_pos(self, open_new=True): '\n 切换合约,从持仓的非主力合约,切换至主力合约\n :param open_new: 是否开仓主力合约\n :return:\n ' if (self.entrust != 0): return if (self.position.long_pos == 0): return if (self.cur_mi_price == 0): return none_mi_grid = None none_mi_symbol = None self.write_log(f'持仓换月=>启动.') for g in self.gt.get_opened_grids(direction=Direction.LONG): none_mi_symbol = g.snapshot.get('mi_symbol', g.vt_symbol) if ((none_mi_symbol is None) or (none_mi_symbol == self.vt_symbol)): self.write_log(f'none_mi_symbol:{none_mi_symbol}, vt_symbol:{self.vt_symbol} 一致,不处理') continue if ((not g.open_status) or g.order_status or ((g.volume - g.traded_volume) <= 0)): self.write_log(f'开仓状态:{g.open_status}, 委托状态:{g.order_status},网格持仓:{g.volume} ,已交易数量:{g.traded_volume}, 不处理') continue none_mi_grid = g if ((g.traded_volume > 0) and ((g.volume - g.traded_volume) > 0)): g.volume -= g.traded_volume g.traded_volume = 0 break if (none_mi_grid is None): return self.write_log(f'持仓换月=>找到多单持仓:{none_mi_symbol},持仓数量:{none_mi_grid.volume}') none_mi_tick = self.tick_dict.get(none_mi_symbol) mi_tick = self.tick_dict.get(self.vt_symbol, None) if ((none_mi_tick is None) or (mi_tick is None)): return if (self.is_upper_limit(none_mi_symbol) or self.is_upper_limit(self.vt_symbol)): self.write_log(f'{none_mi_symbol} 或 {self.vt_symbol} 为涨停价,不做换仓') return none_mi_price = max(none_mi_tick.last_price, none_mi_tick.bid_price_1) grid = deepcopy(none_mi_grid) grid.id = str(uuid.uuid1()) grid.open_status = False self.write_log(f'持仓换月=>复制持仓信息{none_mi_symbol},ID:{none_mi_grid.id} => {self.vt_symbol},ID:{grid.id}') vt_orderids = self.sell(price=none_mi_price, volume=none_mi_grid.volume, vt_symbol=none_mi_symbol, order_type=self.order_type, grid=none_mi_grid) if (len(vt_orderids) > 0): self.write_log(f'持仓换月=>委托卖出非主力合约{none_mi_symbol}持仓:{none_mi_grid.volume}') if none_mi_grid.snapshot.get('switched', False): self.write_log(f'持仓换月=>已经执行过换月,不再创建新的买入操作') return none_mi_grid.snapshot.update({'switched': True}) if (not open_new): self.write_log(f'不买入新的主力合约:{self.vt_symbol},数量:{grid.volume}') self.gt.save() return grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.dn_grids.append(grid) vt_orderids = self.buy(price=(self.cur_mi_price + (5 * self.price_tick)), volume=grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, grid=grid) if (len(vt_orderids) > 0): self.write_log(u'持仓换月=>委托买入主力合约:{},价格:{},数量:{}'.format(self.vt_symbol, self.cur_mi_price, grid.volume)) else: self.write_error(f'持仓换月=>委托买入主力合约:{self.vt_symbol}失败') self.gt.save() else: self.write_error(f'持仓换月=>委托卖出非主力合约:{none_mi_symbol}失败')
def tns_switch_short_pos(self, open_new=True): '\n 切换合约,从持仓的非主力合约,切换至主力合约\n :param open_new: 是否开仓新得主力合约\n :return:\n ' if (self.entrust != 0): return if (self.position.short_pos == 0): return if (self.cur_mi_price == 0): return none_mi_grid = None none_mi_symbol = None for g in self.gt.get_opened_grids(direction=Direction.SHORT): none_mi_symbol = g.snapshot.get('mi_symbol') if ((none_mi_symbol is None) or (none_mi_symbol == self.vt_symbol)): continue if ((not g.open_status) or g.order_status or ((g.volume - g.traded_volume) <= 0)): continue none_mi_grid = g if ((g.traded_volume > 0) and ((g.volume - g.traded_volume) > 0)): g.volume -= g.traded_volume g.traded_volume = 0 break if (none_mi_grid is None): return none_mi_tick = self.tick_dict.get(none_mi_symbol) mi_tick = self.tick_dict.get(self.vt_symbol, None) if ((none_mi_tick is None) or (mi_tick is None)): return if (self.is_lower_limit(none_mi_symbol) or self.is_lower_limit(self.vt_symbol)): return none_mi_price = max(none_mi_tick.last_price, none_mi_tick.bid_price_1) grid = deepcopy(none_mi_grid) grid.id = str(uuid.uuid1()) vt_orderids = self.cover(price=none_mi_price, volume=none_mi_grid.volume, vt_symbol=none_mi_symbol, order_type=self.order_type, grid=none_mi_grid) if (len(vt_orderids) > 0): self.write_log(f'委托平空非主力合约{none_mi_symbol}持仓:{none_mi_grid.volume}') if none_mi_grid.snapshot.get('switched', False): self.write_log(f'已经执行过换月,不再创建新的空操作') return none_mi_grid.snapshot.update({'switched': True}) if (not open_new): self.write_log(f'不开空新的主力合约:{self.vt_symbol},数量:{grid.volume}') self.gt.save() return grid.id = str(uuid.uuid1()) grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.up_grids.append(grid) vt_orderids = self.short(price=self.cur_mi_price, volume=grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, grid=grid) if (len(vt_orderids) > 0): self.write_log(f'委托做空主力合约:{self.vt_symbol},价格:{self.cur_mi_price},数量:{grid.volume}') else: self.write_error(f'委托做空主力合约:{self.vt_symbol}失败') self.gt.save() else: self.write_error(f'委托平空非主力合约:{none_mi_symbol}失败')
-2,319,968,849,727,294,500
切换合约,从持仓的非主力合约,切换至主力合约 :param open_new: 是否开仓新得主力合约 :return:
vnpy/app/cta_strategy_pro/template.py
tns_switch_short_pos
UtorYeung/vnpy
python
def tns_switch_short_pos(self, open_new=True): '\n 切换合约,从持仓的非主力合约,切换至主力合约\n :param open_new: 是否开仓新得主力合约\n :return:\n ' if (self.entrust != 0): return if (self.position.short_pos == 0): return if (self.cur_mi_price == 0): return none_mi_grid = None none_mi_symbol = None for g in self.gt.get_opened_grids(direction=Direction.SHORT): none_mi_symbol = g.snapshot.get('mi_symbol') if ((none_mi_symbol is None) or (none_mi_symbol == self.vt_symbol)): continue if ((not g.open_status) or g.order_status or ((g.volume - g.traded_volume) <= 0)): continue none_mi_grid = g if ((g.traded_volume > 0) and ((g.volume - g.traded_volume) > 0)): g.volume -= g.traded_volume g.traded_volume = 0 break if (none_mi_grid is None): return none_mi_tick = self.tick_dict.get(none_mi_symbol) mi_tick = self.tick_dict.get(self.vt_symbol, None) if ((none_mi_tick is None) or (mi_tick is None)): return if (self.is_lower_limit(none_mi_symbol) or self.is_lower_limit(self.vt_symbol)): return none_mi_price = max(none_mi_tick.last_price, none_mi_tick.bid_price_1) grid = deepcopy(none_mi_grid) grid.id = str(uuid.uuid1()) vt_orderids = self.cover(price=none_mi_price, volume=none_mi_grid.volume, vt_symbol=none_mi_symbol, order_type=self.order_type, grid=none_mi_grid) if (len(vt_orderids) > 0): self.write_log(f'委托平空非主力合约{none_mi_symbol}持仓:{none_mi_grid.volume}') if none_mi_grid.snapshot.get('switched', False): self.write_log(f'已经执行过换月,不再创建新的空操作') return none_mi_grid.snapshot.update({'switched': True}) if (not open_new): self.write_log(f'不开空新的主力合约:{self.vt_symbol},数量:{grid.volume}') self.gt.save() return grid.id = str(uuid.uuid1()) grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.up_grids.append(grid) vt_orderids = self.short(price=self.cur_mi_price, volume=grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, grid=grid) if (len(vt_orderids) > 0): self.write_log(f'委托做空主力合约:{self.vt_symbol},价格:{self.cur_mi_price},数量:{grid.volume}') else: self.write_error(f'委托做空主力合约:{self.vt_symbol}失败') self.gt.save() else: self.write_error(f'委托平空非主力合约:{none_mi_symbol}失败')
def display_grids(self): '更新网格显示信息' if (not self.inited): return up_grids_info = self.gt.to_str(direction=Direction.SHORT) if (len(self.gt.up_grids) > 0): self.write_log(up_grids_info) dn_grids_info = self.gt.to_str(direction=Direction.LONG) if (len(self.gt.dn_grids) > 0): self.write_log(dn_grids_info)
-7,978,628,409,817,070,000
更新网格显示信息
vnpy/app/cta_strategy_pro/template.py
display_grids
UtorYeung/vnpy
python
def display_grids(self): if (not self.inited): return up_grids_info = self.gt.to_str(direction=Direction.SHORT) if (len(self.gt.up_grids) > 0): self.write_log(up_grids_info) dn_grids_info = self.gt.to_str(direction=Direction.LONG) if (len(self.gt.dn_grids) > 0): self.write_log(dn_grids_info)
def display_tns(self): '显示事务的过程记录=》 log' if (not self.inited): return self.write_log(u'{} 当前指数{}价格:{},当前主力{}价格:{}'.format(self.cur_datetime, self.idx_symbol, self.cur_99_price, self.vt_symbol, self.cur_mi_price)) if hasattr(self, 'policy'): policy = getattr(self, 'policy') op = getattr(policy, 'to_json', None) if callable(op): self.write_log(u'当前Policy:{}'.format(policy.to_json()))
-1,050,589,898,606,807,700
显示事务的过程记录=》 log
vnpy/app/cta_strategy_pro/template.py
display_tns
UtorYeung/vnpy
python
def display_tns(self): if (not self.inited): return self.write_log(u'{} 当前指数{}价格:{},当前主力{}价格:{}'.format(self.cur_datetime, self.idx_symbol, self.cur_99_price, self.vt_symbol, self.cur_mi_price)) if hasattr(self, 'policy'): policy = getattr(self, 'policy') op = getattr(policy, 'to_json', None) if callable(op): self.write_log(u'当前Policy:{}'.format(policy.to_json()))
def save_dist(self, dist_data): '\n 保存策略逻辑过程记录=》 csv文件按\n :param dist_data:\n :return:\n ' if self.backtesting: save_path = self.cta_engine.get_logs_path() else: save_path = self.cta_engine.get_data_path() try: if (self.position and ('long_pos' not in dist_data)): dist_data.update({'long_pos': self.position.long_pos}) if (self.position and ('short_pos' not in dist_data)): dist_data.update({'short_pos': self.position.short_pos}) file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_dist.csv')) append_data(file_name=file_name, dict_data=dist_data, field_names=self.dist_fieldnames) except Exception as ex: self.write_error(u'save_dist 异常:{} {}'.format(str(ex), traceback.format_exc()))
6,534,891,654,396,555,000
保存策略逻辑过程记录=》 csv文件按 :param dist_data: :return:
vnpy/app/cta_strategy_pro/template.py
save_dist
UtorYeung/vnpy
python
def save_dist(self, dist_data): '\n 保存策略逻辑过程记录=》 csv文件按\n :param dist_data:\n :return:\n ' if self.backtesting: save_path = self.cta_engine.get_logs_path() else: save_path = self.cta_engine.get_data_path() try: if (self.position and ('long_pos' not in dist_data)): dist_data.update({'long_pos': self.position.long_pos}) if (self.position and ('short_pos' not in dist_data)): dist_data.update({'short_pos': self.position.short_pos}) file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_dist.csv')) append_data(file_name=file_name, dict_data=dist_data, field_names=self.dist_fieldnames) except Exception as ex: self.write_error(u'save_dist 异常:{} {}'.format(str(ex), traceback.format_exc()))
def save_tns(self, tns_data): '\n 保存多空事务记录=》csv文件,便于后续分析\n :param tns_data: {"datetime":xxx, "direction":"long"或者"short", "price":xxx}\n :return:\n ' if self.backtesting: save_path = self.cta_engine.get_logs_path() else: save_path = self.cta_engine.get_data_path() try: file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_tns.csv')) append_data(file_name=file_name, dict_data=tns_data) except Exception as ex: self.write_error(u'save_tns 异常:{} {}'.format(str(ex), traceback.format_exc()))
3,043,168,499,550,988,000
保存多空事务记录=》csv文件,便于后续分析 :param tns_data: {"datetime":xxx, "direction":"long"或者"short", "price":xxx} :return:
vnpy/app/cta_strategy_pro/template.py
save_tns
UtorYeung/vnpy
python
def save_tns(self, tns_data): '\n 保存多空事务记录=》csv文件,便于后续分析\n :param tns_data: {"datetime":xxx, "direction":"long"或者"short", "price":xxx}\n :return:\n ' if self.backtesting: save_path = self.cta_engine.get_logs_path() else: save_path = self.cta_engine.get_data_path() try: file_name = os.path.abspath(os.path.join(save_path, f'{self.strategy_name}_tns.csv')) append_data(file_name=file_name, dict_data=tns_data) except Exception as ex: self.write_error(u'save_tns 异常:{} {}'.format(str(ex), traceback.format_exc()))
def send_wechat(self, msg: str): '实盘时才发送微信' if self.backtesting: return self.cta_engine.send_wechat(msg=msg, strategy=self)
4,796,113,719,315,409,000
实盘时才发送微信
vnpy/app/cta_strategy_pro/template.py
send_wechat
UtorYeung/vnpy
python
def send_wechat(self, msg: str): if self.backtesting: return self.cta_engine.send_wechat(msg=msg, strategy=self)
def update_setting(self, setting: dict): '更新配置参数' super().update_setting(setting) if (not self.backtesting): if self.activate_fak: self.order_type = OrderType.FAK
8,905,263,113,275,753,000
更新配置参数
vnpy/app/cta_strategy_pro/template.py
update_setting
UtorYeung/vnpy
python
def update_setting(self, setting: dict): super().update_setting(setting) if (not self.backtesting): if self.activate_fak: self.order_type = OrderType.FAK
def load_policy(self): '加载policy' if self.policy: self.write_log(u'load_policy(),初始化Policy') self.policy.load() self.write_log(u'Policy:{}'.format(self.policy.to_json()))
-1,173,244,053,206,510,600
加载policy
vnpy/app/cta_strategy_pro/template.py
load_policy
UtorYeung/vnpy
python
def load_policy(self): if self.policy: self.write_log(u'load_policy(),初始化Policy') self.policy.load() self.write_log(u'Policy:{}'.format(self.policy.to_json()))
def on_start(self): '启动策略(必须由用户继承实现)' self.write_log(u'启动') self.trading = True self.put_event()
-5,815,070,311,948,098,000
启动策略(必须由用户继承实现)
vnpy/app/cta_strategy_pro/template.py
on_start
UtorYeung/vnpy
python
def on_start(self): self.write_log(u'启动') self.trading = True self.put_event()
def on_stop(self): '停止策略(必须由用户继承实现)' self.active_orders.clear() self.pos = 0 self.entrust = 0 self.write_log(u'停止') self.put_event()
-7,447,439,848,410,118,000
停止策略(必须由用户继承实现)
vnpy/app/cta_strategy_pro/template.py
on_stop
UtorYeung/vnpy
python
def on_stop(self): self.active_orders.clear() self.pos = 0 self.entrust = 0 self.write_log(u'停止') self.put_event()
def on_trade(self, trade: TradeData): '\n 交易更新\n 支持股指期货的对锁单或者解锁\n :param trade:\n :return:\n ' self.write_log(u'{},交易更新 =>{},\n 当前持仓:{} '.format(self.cur_datetime, trade.__dict__, self.position.pos)) dist_record = dict() if self.backtesting: dist_record['datetime'] = trade.time else: dist_record['datetime'] = ' '.join([self.cur_datetime.strftime('%Y-%m-%d'), trade.time]) dist_record['volume'] = trade.volume dist_record['price'] = trade.price dist_record['symbol'] = trade.vt_symbol if ((trade.exchange == Exchange.CFFEX) and (not self.backtesting)): if (trade.direction == Direction.LONG): if (abs(self.position.short_pos) >= trade.volume): self.position.short_pos += trade.volume else: self.position.long_pos += trade.volume elif (self.position.long_pos >= trade.volume): self.position.long_pos -= trade.volume else: self.position.short_pos -= trade.volume self.position.pos = (self.position.long_pos + self.position.short_pos) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos else: if ((trade.direction == Direction.LONG) and (trade.offset == Offset.OPEN)): dist_record['operation'] = 'buy' self.position.open_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.SHORT) and (trade.offset == Offset.OPEN)): dist_record['operation'] = 'short' self.position.open_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.LONG) and (trade.offset != Offset.OPEN)): dist_record['operation'] = 'cover' self.position.close_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.SHORT) and (trade.offset != Offset.OPEN)): dist_record['operation'] = 'sell' self.position.close_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos self.save_dist(dist_record) self.pos = self.position.pos
-1,073,388,828,579,977,900
交易更新 支持股指期货的对锁单或者解锁 :param trade: :return:
vnpy/app/cta_strategy_pro/template.py
on_trade
UtorYeung/vnpy
python
def on_trade(self, trade: TradeData): '\n 交易更新\n 支持股指期货的对锁单或者解锁\n :param trade:\n :return:\n ' self.write_log(u'{},交易更新 =>{},\n 当前持仓:{} '.format(self.cur_datetime, trade.__dict__, self.position.pos)) dist_record = dict() if self.backtesting: dist_record['datetime'] = trade.time else: dist_record['datetime'] = ' '.join([self.cur_datetime.strftime('%Y-%m-%d'), trade.time]) dist_record['volume'] = trade.volume dist_record['price'] = trade.price dist_record['symbol'] = trade.vt_symbol if ((trade.exchange == Exchange.CFFEX) and (not self.backtesting)): if (trade.direction == Direction.LONG): if (abs(self.position.short_pos) >= trade.volume): self.position.short_pos += trade.volume else: self.position.long_pos += trade.volume elif (self.position.long_pos >= trade.volume): self.position.long_pos -= trade.volume else: self.position.short_pos -= trade.volume self.position.pos = (self.position.long_pos + self.position.short_pos) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos else: if ((trade.direction == Direction.LONG) and (trade.offset == Offset.OPEN)): dist_record['operation'] = 'buy' self.position.open_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.SHORT) and (trade.offset == Offset.OPEN)): dist_record['operation'] = 'short' self.position.open_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.LONG) and (trade.offset != Offset.OPEN)): dist_record['operation'] = 'cover' self.position.close_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos if ((trade.direction == Direction.SHORT) and (trade.offset != Offset.OPEN)): dist_record['operation'] = 'sell' self.position.close_pos(trade.direction, volume=trade.volume) dist_record['long_pos'] = self.position.long_pos dist_record['short_pos'] = self.position.short_pos self.save_dist(dist_record) self.pos = self.position.pos
def fix_order(self, order: OrderData): '修正order被拆单得情况' order_info = self.active_orders.get(order.vt_orderid, None) if order_info: volume = order_info.get('volume') if (volume != order.volume): self.write_log(f'修正order被拆单得情况,调整{order.vt_orderid} volume:{volume}=>{order.volume}') order_info.update({'volume': order.volume})
-1,537,104,623,017,642,000
修正order被拆单得情况
vnpy/app/cta_strategy_pro/template.py
fix_order
UtorYeung/vnpy
python
def fix_order(self, order: OrderData): order_info = self.active_orders.get(order.vt_orderid, None) if order_info: volume = order_info.get('volume') if (volume != order.volume): self.write_log(f',调整{order.vt_orderid} volume:{volume}=>{order.volume}') order_info.update({'volume': order.volume})
def on_order(self, order: OrderData): '报单更新' self.write_log(u'{}报单更新 => {}'.format(self.cur_datetime, order.__dict__)) self.fix_order(order) if (order.vt_orderid in self.active_orders): active_order = self.active_orders[order.vt_orderid] if ((order.volume == order.traded) and (order.status in [Status.ALLTRADED])): self.on_order_all_traded(order) elif ((active_order['offset'] == Offset.OPEN) and (order.status in [Status.CANCELLED])): self.on_order_open_canceled(order) elif ((active_order['offset'] != Offset.OPEN) and (order.status in [Status.CANCELLED])): self.on_order_close_canceled(order) elif (order.status == Status.REJECTED): if (active_order['offset'] == Offset.OPEN): self.write_error(u'{}委托单开{}被拒,price:{},total:{},traded:{},status:{}'.format(order.vt_symbol, order.direction, order.price, order.volume, order.traded, order.status)) self.on_order_open_canceled(order) else: self.write_error(u'OnOrder({})委托单平{}被拒,price:{},total:{},traded:{},status:{}'.format(order.vt_symbol, order.direction, order.price, order.volume, order.traded, order.status)) self.on_order_close_canceled(order) else: self.write_log(u'委托单未完成,total:{},traded:{},tradeStatus:{}'.format(order.volume, order.traded, order.status)) else: self.write_error(u'委托单{}不在策略的未完成订单列表中:{}'.format(order.vt_orderid, self.active_orders))
-6,724,270,649,476,190,000
报单更新
vnpy/app/cta_strategy_pro/template.py
on_order
UtorYeung/vnpy
python
def on_order(self, order: OrderData): self.write_log(u'{} => {}'.format(self.cur_datetime, order.__dict__)) self.fix_order(order) if (order.vt_orderid in self.active_orders): active_order = self.active_orders[order.vt_orderid] if ((order.volume == order.traded) and (order.status in [Status.ALLTRADED])): self.on_order_all_traded(order) elif ((active_order['offset'] == Offset.OPEN) and (order.status in [Status.CANCELLED])): self.on_order_open_canceled(order) elif ((active_order['offset'] != Offset.OPEN) and (order.status in [Status.CANCELLED])): self.on_order_close_canceled(order) elif (order.status == Status.REJECTED): if (active_order['offset'] == Offset.OPEN): self.write_error(u'{}委托单开{}被拒,price:{},total:{},traded:{},status:{}'.format(order.vt_symbol, order.direction, order.price, order.volume, order.traded, order.status)) self.on_order_open_canceled(order) else: self.write_error(u'OnOrder({})委托单平{}被拒,price:{},total:{},traded:{},status:{}'.format(order.vt_symbol, order.direction, order.price, order.volume, order.traded, order.status)) self.on_order_close_canceled(order) else: self.write_log(u'委托单未完成,total:{},traded:{},tradeStatus:{}'.format(order.volume, order.traded, order.status)) else: self.write_error(u'委托单{}不在策略的未完成订单列表中:{}'.format(order.vt_orderid, self.active_orders))
def on_order_all_traded(self, order: OrderData): '\n 订单全部成交\n :param order:\n :return:\n ' self.write_log(u'报单更新 => 委托单全部完成:{}'.format(order.__dict__)) active_order = self.active_orders[order.vt_orderid] grid = active_order.get('grid', None) if (grid is not None): if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (len(grid.order_ids) == 0): grid.order_status = False grid.traded_volume = 0 if (active_order['offset'] != Offset.OPEN): grid.open_status = False grid.close_status = True grid.open_time = None self.write_log((f'{grid.direction.value}单已平仓完毕,order_price:{order.price}' + f',volume:{order.volume}')) self.write_log(f'移除网格:{grid.to_json()}') self.gt.remove_grids_by_ids(direction=grid.direction, ids=[grid.id]) else: grid.open_status = True grid.open_time = self.cur_datetime self.write_log((f'{grid.direction.value}单已开仓完毕,order_price:{order.price}' + f',volume:{order.volume}')) else: old_traded_volume = grid.traded_volume grid.traded_volume += order.volume self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(f'剩余委托单号:{grid.order_ids}') self.gt.save() else: self.write_error(f'on_trade找不到对应grid') self.active_orders.pop(order.vt_orderid, None)
7,630,674,831,798,039,000
订单全部成交 :param order: :return:
vnpy/app/cta_strategy_pro/template.py
on_order_all_traded
UtorYeung/vnpy
python
def on_order_all_traded(self, order: OrderData): '\n 订单全部成交\n :param order:\n :return:\n ' self.write_log(u'报单更新 => 委托单全部完成:{}'.format(order.__dict__)) active_order = self.active_orders[order.vt_orderid] grid = active_order.get('grid', None) if (grid is not None): if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (len(grid.order_ids) == 0): grid.order_status = False grid.traded_volume = 0 if (active_order['offset'] != Offset.OPEN): grid.open_status = False grid.close_status = True grid.open_time = None self.write_log((f'{grid.direction.value}单已平仓完毕,order_price:{order.price}' + f',volume:{order.volume}')) self.write_log(f'移除网格:{grid.to_json()}') self.gt.remove_grids_by_ids(direction=grid.direction, ids=[grid.id]) else: grid.open_status = True grid.open_time = self.cur_datetime self.write_log((f'{grid.direction.value}单已开仓完毕,order_price:{order.price}' + f',volume:{order.volume}')) else: old_traded_volume = grid.traded_volume grid.traded_volume += order.volume self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(f'剩余委托单号:{grid.order_ids}') self.gt.save() else: self.write_error(f'on_trade找不到对应grid') self.active_orders.pop(order.vt_orderid, None)
def on_order_open_canceled(self, order: OrderData): '\n 委托开仓单撤销\n 如果是FAK模式,重新修改价格,再提交\n FAK用于实盘,需要增加涨跌停判断\n :param order:\n :return:\n ' self.write_log(u'报单更新 => 委托开仓 => 撤销:{}'.format(order.__dict__)) if (not self.trading): if (not self.backtesting): self.write_error(u'当前不允许交易') return if (order.vt_orderid not in self.active_orders): self.write_error(u'{}不在未完成的委托单中{}。'.format(order.vt_orderid, self.active_orders)) return old_order = self.active_orders[order.vt_orderid] self.write_log(u'报单更新 => {} 未完成订单信息:{}'.format(order.vt_orderid, old_order)) old_order['traded'] = order.traded order_vt_symbol = copy(old_order['vt_symbol']) order_volume = (old_order['volume'] - old_order['traded']) order_price = old_order['price'] order_type = old_order.get('order_type', OrderType.LIMIT) order_retry = old_order.get('retry', 0) grid = old_order.get('grid', None) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (order_volume <= 0): msg = u'{} {}{}需重新开仓数量为{},不再开仓'.format(self.strategy_name, order.vt_orderid, order_vt_symbol, order_volume) self.write_error(msg) self.write_log(u'移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return if (order_retry > 20): msg = u'{} {}/{}手, 重试开仓次数{}>20'.format(self.strategy_name, order_vt_symbol, order_volume, order_retry) self.write_error(msg) self.send_wechat(msg) if (len(grid.order_ids) == 0): grid.order_status = False self.gt.save() self.write_log(u'网格信息更新:{}'.format(grid.__dict__)) self.write_log(u'移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return order_retry += 1 if ((old_order['direction'] == Direction.LONG) and (order_type == OrderType.FAK)): self.write_log(u'移除旧的委托记录:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送buy委托.grid:{}'.format(grid.__dict__)) buy_price = (max(self.cur_mi_tick.ask_price_1, self.cur_mi_tick.last_price, order_price) + self.price_tick) if ((self.cur_mi_tick.limit_up > 0) and (buy_price > self.cur_mi_tick.limit_up)): buy_price = self.cur_mi_tick.limit_up if self.is_upper_limit(self.vt_symbol): self.write_log(u'{}涨停,不做buy'.format(self.vt_symbol)) return vt_orderids = self.buy(price=buy_price, volume=order_volume, vt_symbol=self.vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手开多单,价格:{},失败'.format(self.vt_symbol, order_volume, buy_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid, None) info.update({'retry': order_retry}) self.gt.save() elif ((old_order['direction'] == Direction.SHORT) and (order_type == OrderType.FAK)): self.write_log(u'移除旧的委托记录:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送short委托.grid:{}'.format(grid.__dict__)) short_price = (min(self.cur_mi_tick.bid_price_1, self.cur_mi_tick.last_price, order_price) - self.price_tick) if ((self.cur_mi_tick.limit_down > 0) and (short_price < self.cur_mi_tick.limit_down)): short_price = self.cur_mi_tick.limit_down if self.is_lower_limit(self.vt_symbol): self.write_log(u'{}跌停,不做short'.format(self.vt_symbol)) return vt_orderids = self.short(price=short_price, volume=order_volume, vt_symbol=self.vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手开空单,价格:{}, 失败'.format(self.vt_symbol, order_volume, short_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid, None) info.update({'retry': order_retry}) self.gt.save() else: pre_status = old_order.get('status', Status.NOTTRADED) old_order.update({'status': Status.CANCELLED}) self.write_log(u'委托单方式{},状态:{}=>{}'.format(order_type, pre_status, old_order.get('status'))) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (not grid.order_ids): grid.order_status = False self.gt.save() self.active_orders.update({order.vt_orderid: old_order}) self.display_grids()
-5,928,078,699,495,564,000
委托开仓单撤销 如果是FAK模式,重新修改价格,再提交 FAK用于实盘,需要增加涨跌停判断 :param order: :return:
vnpy/app/cta_strategy_pro/template.py
on_order_open_canceled
UtorYeung/vnpy
python
def on_order_open_canceled(self, order: OrderData): '\n 委托开仓单撤销\n 如果是FAK模式,重新修改价格,再提交\n FAK用于实盘,需要增加涨跌停判断\n :param order:\n :return:\n ' self.write_log(u'报单更新 => 委托开仓 => 撤销:{}'.format(order.__dict__)) if (not self.trading): if (not self.backtesting): self.write_error(u'当前不允许交易') return if (order.vt_orderid not in self.active_orders): self.write_error(u'{}不在未完成的委托单中{}。'.format(order.vt_orderid, self.active_orders)) return old_order = self.active_orders[order.vt_orderid] self.write_log(u'报单更新 => {} 未完成订单信息:{}'.format(order.vt_orderid, old_order)) old_order['traded'] = order.traded order_vt_symbol = copy(old_order['vt_symbol']) order_volume = (old_order['volume'] - old_order['traded']) order_price = old_order['price'] order_type = old_order.get('order_type', OrderType.LIMIT) order_retry = old_order.get('retry', 0) grid = old_order.get('grid', None) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (order_volume <= 0): msg = u'{} {}{}需重新开仓数量为{},不再开仓'.format(self.strategy_name, order.vt_orderid, order_vt_symbol, order_volume) self.write_error(msg) self.write_log(u'移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return if (order_retry > 20): msg = u'{} {}/{}手, 重试开仓次数{}>20'.format(self.strategy_name, order_vt_symbol, order_volume, order_retry) self.write_error(msg) self.send_wechat(msg) if (len(grid.order_ids) == 0): grid.order_status = False self.gt.save() self.write_log(u'网格信息更新:{}'.format(grid.__dict__)) self.write_log(u'移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return order_retry += 1 if ((old_order['direction'] == Direction.LONG) and (order_type == OrderType.FAK)): self.write_log(u'移除旧的委托记录:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送buy委托.grid:{}'.format(grid.__dict__)) buy_price = (max(self.cur_mi_tick.ask_price_1, self.cur_mi_tick.last_price, order_price) + self.price_tick) if ((self.cur_mi_tick.limit_up > 0) and (buy_price > self.cur_mi_tick.limit_up)): buy_price = self.cur_mi_tick.limit_up if self.is_upper_limit(self.vt_symbol): self.write_log(u'{}涨停,不做buy'.format(self.vt_symbol)) return vt_orderids = self.buy(price=buy_price, volume=order_volume, vt_symbol=self.vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手开多单,价格:{},失败'.format(self.vt_symbol, order_volume, buy_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid, None) info.update({'retry': order_retry}) self.gt.save() elif ((old_order['direction'] == Direction.SHORT) and (order_type == OrderType.FAK)): self.write_log(u'移除旧的委托记录:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送short委托.grid:{}'.format(grid.__dict__)) short_price = (min(self.cur_mi_tick.bid_price_1, self.cur_mi_tick.last_price, order_price) - self.price_tick) if ((self.cur_mi_tick.limit_down > 0) and (short_price < self.cur_mi_tick.limit_down)): short_price = self.cur_mi_tick.limit_down if self.is_lower_limit(self.vt_symbol): self.write_log(u'{}跌停,不做short'.format(self.vt_symbol)) return vt_orderids = self.short(price=short_price, volume=order_volume, vt_symbol=self.vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手开空单,价格:{}, 失败'.format(self.vt_symbol, order_volume, short_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid, None) info.update({'retry': order_retry}) self.gt.save() else: pre_status = old_order.get('status', Status.NOTTRADED) old_order.update({'status': Status.CANCELLED}) self.write_log(u'委托单方式{},状态:{}=>{}'.format(order_type, pre_status, old_order.get('status'))) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (not grid.order_ids): grid.order_status = False self.gt.save() self.active_orders.update({order.vt_orderid: old_order}) self.display_grids()
def on_order_close_canceled(self, order: OrderData): '委托平仓单撤销' self.write_log(u'报单更新 => 委托平仓 => 撤销:{}'.format(order.__dict__)) if (order.vt_orderid not in self.active_orders): self.write_error(u'{}不在未完成的委托单中:{}。'.format(order.vt_orderid, self.active_orders)) return if (not self.trading): self.write_error(u'当前不允许交易') return old_order = self.active_orders[order.vt_orderid] self.write_log(u'报单更新 => {} 未完成订单信息:{}'.format(order.vt_orderid, old_order)) old_order['traded'] = order.traded order_vt_symbol = copy(old_order['vt_symbol']) order_volume = (old_order['volume'] - old_order['traded']) order_price = old_order['price'] order_type = old_order.get('order_type', OrderType.LIMIT) order_retry = old_order.get('retry', 0) grid = old_order.get('grid', None) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (order_volume <= 0): msg = u'{} {}{}重新平仓数量为{},不再平仓'.format(self.strategy_name, order.vt_orderid, order_vt_symbol, order_volume) self.write_error(msg) self.send_wechat(msg) self.write_log(u'活动订单移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return if (order_retry > 20): msg = u'{} 平仓撤单 {}/{}手, 重试平仓次数{}>20'.format(self.strategy_name, order_vt_symbol, order_volume, order_retry) self.write_error(msg) self.send_wechat(msg) if (not grid.order_ids): grid.order_status = False self.gt.save() self.write_log(u'更新网格=>{}'.format(grid.__dict__)) self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return order_retry += 1 if ((old_order['direction'] == Direction.LONG) and (order_type == OrderType.FAK)): self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送cover委托.grid:{}'.format(grid.__dict__)) cover_tick = self.tick_dict.get(order_vt_symbol, self.cur_mi_tick) cover_price = (max(cover_tick.ask_price_1, cover_tick.last_price, order_price) + self.price_tick) if ((cover_tick.limit_up > 0) and (cover_price > cover_tick.limit_up)): cover_price = cover_tick.limit_up if self.is_upper_limit(order_vt_symbol): self.write_log(u'{}涨停,不做cover'.format(order_vt_symbol)) return pos = self.cta_engine.get_position_holding(vt_symbol=order_vt_symbol) if (pos is None): self.write_error(f'{self.strategy_name}无法获取{order_vt_symbol}的持仓信息,无法平仓') return if (pos.short_pos < order_volume): self.write_error(f'{self.strategy_name}{order_vt_symbol}的持仓空单{pos.short_pos}不满足平仓{order_volume}要求,无法平仓') return vt_orderids = self.cover(price=cover_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手平空单{}失败'.format(order_vt_symbol, order_volume, cover_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid) info.update({'retry': order_retry}) self.gt.save() elif ((old_order['direction'] == Direction.SHORT) and (order_type == OrderType.FAK)): self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送sell委托.grid:{}'.format(grid.__dict__)) sell_tick = self.tick_dict.get(order_vt_symbol, self.cur_mi_tick) sell_price = (min(sell_tick.bid_price_1, sell_tick.last_price, order_price) - self.price_tick) if ((sell_tick.limit_down > 0) and (sell_price < sell_tick.limit_down)): sell_price = sell_tick.limit_down if self.is_lower_limit(order_vt_symbol): self.write_log(u'{}涨停,不做sell'.format(order_vt_symbol)) return pos = self.cta_engine.get_position_holding(vt_symbol=order_vt_symbol) if (pos is None): self.write_error(f'{self.strategy_name}无法获取{order_vt_symbol}的持仓信息,无法平仓') return if (pos.long_pos < order_volume): self.write_error(f'{self.strategy_name}{order_vt_symbol}的持仓多单{pos.long_pos}不满足平仓{order_volume}要求,无法平仓') return vt_orderids = self.sell(price=sell_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手平多单{}失败'.format(order_vt_symbol, order_volume, sell_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid) info.update({'retry': order_retry}) self.gt.save() else: pre_status = old_order.get('status', Status.NOTTRADED) old_order.update({'status': Status.CANCELLED}) self.write_log(u'委托单状态:{}=>{}'.format(pre_status, old_order.get('status'))) if grid: if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (len(grid.order_ids) == 0): grid.order_status = False self.gt.save() self.active_orders.update({order.vt_orderid: old_order}) self.display_grids()
2,936,763,617,247,122,400
委托平仓单撤销
vnpy/app/cta_strategy_pro/template.py
on_order_close_canceled
UtorYeung/vnpy
python
def on_order_close_canceled(self, order: OrderData): self.write_log(u'报单更新 => 委托平仓 => 撤销:{}'.format(order.__dict__)) if (order.vt_orderid not in self.active_orders): self.write_error(u'{}不在未完成的委托单中:{}。'.format(order.vt_orderid, self.active_orders)) return if (not self.trading): self.write_error(u'当前不允许交易') return old_order = self.active_orders[order.vt_orderid] self.write_log(u'报单更新 => {} 未完成订单信息:{}'.format(order.vt_orderid, old_order)) old_order['traded'] = order.traded order_vt_symbol = copy(old_order['vt_symbol']) order_volume = (old_order['volume'] - old_order['traded']) order_price = old_order['price'] order_type = old_order.get('order_type', OrderType.LIMIT) order_retry = old_order.get('retry', 0) grid = old_order.get('grid', None) if grid: if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (order_volume <= 0): msg = u'{} {}{}重新平仓数量为{},不再平仓'.format(self.strategy_name, order.vt_orderid, order_vt_symbol, order_volume) self.write_error(msg) self.send_wechat(msg) self.write_log(u'活动订单移除:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return if (order_retry > 20): msg = u'{} 平仓撤单 {}/{}手, 重试平仓次数{}>20'.format(self.strategy_name, order_vt_symbol, order_volume, order_retry) self.write_error(msg) self.send_wechat(msg) if (not grid.order_ids): grid.order_status = False self.gt.save() self.write_log(u'更新网格=>{}'.format(grid.__dict__)) self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) return order_retry += 1 if ((old_order['direction'] == Direction.LONG) and (order_type == OrderType.FAK)): self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送cover委托.grid:{}'.format(grid.__dict__)) cover_tick = self.tick_dict.get(order_vt_symbol, self.cur_mi_tick) cover_price = (max(cover_tick.ask_price_1, cover_tick.last_price, order_price) + self.price_tick) if ((cover_tick.limit_up > 0) and (cover_price > cover_tick.limit_up)): cover_price = cover_tick.limit_up if self.is_upper_limit(order_vt_symbol): self.write_log(u'{}涨停,不做cover'.format(order_vt_symbol)) return pos = self.cta_engine.get_position_holding(vt_symbol=order_vt_symbol) if (pos is None): self.write_error(f'{self.strategy_name}无法获取{order_vt_symbol}的持仓信息,无法平仓') return if (pos.short_pos < order_volume): self.write_error(f'{self.strategy_name}{order_vt_symbol}的持仓空单{pos.short_pos}不满足平仓{order_volume}要求,无法平仓') return vt_orderids = self.cover(price=cover_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手平空单{}失败'.format(order_vt_symbol, order_volume, cover_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid) info.update({'retry': order_retry}) self.gt.save() elif ((old_order['direction'] == Direction.SHORT) and (order_type == OrderType.FAK)): self.write_log(u'移除活动订单:{}'.format(order.vt_orderid)) self.active_orders.pop(order.vt_orderid, None) if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) self.write_log(u'FAK模式,需要重新发送sell委托.grid:{}'.format(grid.__dict__)) sell_tick = self.tick_dict.get(order_vt_symbol, self.cur_mi_tick) sell_price = (min(sell_tick.bid_price_1, sell_tick.last_price, order_price) - self.price_tick) if ((sell_tick.limit_down > 0) and (sell_price < sell_tick.limit_down)): sell_price = sell_tick.limit_down if self.is_lower_limit(order_vt_symbol): self.write_log(u'{}涨停,不做sell'.format(order_vt_symbol)) return pos = self.cta_engine.get_position_holding(vt_symbol=order_vt_symbol) if (pos is None): self.write_error(f'{self.strategy_name}无法获取{order_vt_symbol}的持仓信息,无法平仓') return if (pos.long_pos < order_volume): self.write_error(f'{self.strategy_name}{order_vt_symbol}的持仓多单{pos.long_pos}不满足平仓{order_volume}要求,无法平仓') return vt_orderids = self.sell(price=sell_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=OrderType.FAK, order_time=self.cur_datetime, grid=grid) if (not vt_orderids): self.write_error(u'重新提交{} {}手平多单{}失败'.format(order_vt_symbol, order_volume, sell_price)) return for vt_orderid in vt_orderids: info = self.active_orders.get(vt_orderid) info.update({'retry': order_retry}) self.gt.save() else: pre_status = old_order.get('status', Status.NOTTRADED) old_order.update({'status': Status.CANCELLED}) self.write_log(u'委托单状态:{}=>{}'.format(pre_status, old_order.get('status'))) if grid: if (order.traded > 0): old_traded_volume = grid.traded_volume grid.traded_volume += order.traded self.write_log((f'{grid.direction.value}单部分{order.offset}仓,' + f'网格volume:{grid.volume}, traded_volume:{old_traded_volume}=>{grid.traded_volume}')) if (order.vt_orderid in grid.order_ids): grid.order_ids.remove(order.vt_orderid) if (len(grid.order_ids) == 0): grid.order_status = False self.gt.save() self.active_orders.update({order.vt_orderid: old_order}) self.display_grids()
def on_stop_order(self, stop_order: StopOrder): '\n 停止单更新\n 需要自己重载,处理各类触发、撤单等情况\n ' self.write_log(f'停止单触发:{stop_order.__dict__}')
3,201,659,518,581,367,000
停止单更新 需要自己重载,处理各类触发、撤单等情况
vnpy/app/cta_strategy_pro/template.py
on_stop_order
UtorYeung/vnpy
python
def on_stop_order(self, stop_order: StopOrder): '\n 停止单更新\n 需要自己重载,处理各类触发、撤单等情况\n ' self.write_log(f'停止单触发:{stop_order.__dict__}')
def cancel_all_orders(self): '\n 重载撤销所有正在进行得委托\n :return:\n ' self.write_log(u'撤销所有正在进行得委托') self.tns_cancel_logic(dt=datetime.now(), force=True, reopen=False)
5,625,485,002,846,538,000
重载撤销所有正在进行得委托 :return:
vnpy/app/cta_strategy_pro/template.py
cancel_all_orders
UtorYeung/vnpy
python
def cancel_all_orders(self): '\n 重载撤销所有正在进行得委托\n :return:\n ' self.write_log(u'撤销所有正在进行得委托') self.tns_cancel_logic(dt=datetime.now(), force=True, reopen=False)
def tns_cancel_logic(self, dt, force=False, reopen=False): '撤单逻辑' if (len(self.active_orders) < 1): self.entrust = 0 return canceled_ids = [] for vt_orderid in list(self.active_orders.keys()): order_info = self.active_orders[vt_orderid] order_vt_symbol = order_info.get('vt_symbol', self.vt_symbol) order_time = order_info['order_time'] order_volume = (order_info['volume'] - order_info['traded']) order_grid = order_info['grid'] order_status = order_info.get('status', Status.NOTTRADED) order_type = order_info.get('order_type', OrderType.LIMIT) over_seconds = (dt - order_time).total_seconds() if ((order_status in [Status.NOTTRADED, Status.SUBMITTING]) and ((order_type == OrderType.LIMIT) or ('.SPD' in order_vt_symbol))): if ((over_seconds > self.cancel_seconds) or force): self.write_log(u'撤单逻辑 => 超时{}秒未成交,取消委托单:vt_orderid:{},order:{}'.format(over_seconds, vt_orderid, order_info)) order_info.update({'status': Status.CANCELLING}) self.active_orders.update({vt_orderid: order_info}) ret = self.cancel_order(str(vt_orderid)) if (not ret): self.write_error(f'{self.strategy_name}撤单逻辑 => {order_vt_symbol}撤单失败') continue elif (order_status == Status.CANCELLED): self.write_log(u'撤单逻辑 => 委托单{}已成功撤单,将删除未完成订单{}'.format(vt_orderid, order_info)) canceled_ids.append(vt_orderid) if reopen: if (order_info['offset'] == Offset.OPEN): self.write_log(u'撤单逻辑 => 重新开仓') if (order_info['direction'] == Direction.SHORT): short_price = (self.cur_mi_price - self.price_tick) if ((order_grid.volume != order_volume) and (order_volume > 0)): self.write_log(u'网格volume:{},order_volume:{}不一致,修正'.format(order_grid.volume, order_volume)) order_grid.volume = order_volume self.write_log(u'重新提交{}开空委托,开空价{},v:{}'.format(order_vt_symbol, short_price, order_volume)) vt_orderids = self.short(price=short_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderid:{}'.format(vt_orderids)) order_grid.snapshot.update({'open_price': short_price}) else: self.write_error(u'撤单后,重新委托开空仓失败') else: buy_price = (self.cur_mi_price + self.price_tick) if ((order_grid.volume != order_volume) and (order_volume > 0)): self.write_log(u'网格volume:{},order_volume:{}不一致,修正'.format(order_grid.volume, order_volume)) order_grid.volume = order_volume self.write_log(u'重新提交{}开多委托,开多价{},v:{}'.format(order_vt_symbol, buy_price, order_volume)) vt_orderids = self.buy(price=buy_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) order_grid.snapshot.update({'open_price': buy_price}) else: self.write_error(u'撤单后,重新委托开多仓失败') elif (order_info['direction'] == Direction.SHORT): sell_price = (self.cur_mi_price - self.price_tick) self.write_log(u'重新提交{}平多委托,{},v:{}'.format(order_vt_symbol, sell_price, order_volume)) vt_orderids = self.sell(price=sell_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) else: self.write_error(u'撤单后,重新委托平多仓失败') else: cover_price = (self.cur_mi_price + self.price_tick) self.write_log(u'重新提交{}平空委托,委托价{},v:{}'.format(order_vt_symbol, cover_price, order_volume)) vt_orderids = self.cover(price=cover_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) else: self.write_error(u'撤单后,重新委托平空仓失败') else: self.write_log(u'撤单逻辑 => 无须重新开仓') if ((order_info['offset'] == Offset.OPEN) and order_grid and (len(order_grid.order_ids) == 0)): if ((order_info['traded'] == 0) and (order_grid.traded_volume == 0)): self.write_log(u'撤单逻辑 => 无任何成交 => 移除委托网格{}'.format(order_grid.__dict__)) order_info['grid'] = None self.gt.remove_grids_by_ids(direction=order_grid.direction, ids=[order_grid.id]) elif (order_info['traded'] > 0): self.write_log('撤单逻辑 = > 部分开仓') if (order_grid.traded_volume < order_info['traded']): self.write_log('撤单逻辑 = > 调整网格开仓数 {} => {}'.format(order_grid.traded_volume, order_info['traded'])) order_grid.traded_volume = order_info['traded'] self.write_log(f'撤单逻辑 => 调整网格委托状态=> False, 开仓状态:True, 开仓数量:{order_grid.volume}=>{order_grid.traded_volume}') order_grid.order_status = False order_grid.open_status = True order_grid.volume = order_grid.traded_volume order_grid.traded_volume = 0 for vt_orderid in canceled_ids: self.write_log(u'撤单逻辑 => 删除未完成订单:{}'.format(vt_orderid)) self.active_orders.pop(vt_orderid, None) if (len(self.active_orders) == 0): self.entrust = 0
5,498,034,421,116,743,000
撤单逻辑
vnpy/app/cta_strategy_pro/template.py
tns_cancel_logic
UtorYeung/vnpy
python
def tns_cancel_logic(self, dt, force=False, reopen=False): if (len(self.active_orders) < 1): self.entrust = 0 return canceled_ids = [] for vt_orderid in list(self.active_orders.keys()): order_info = self.active_orders[vt_orderid] order_vt_symbol = order_info.get('vt_symbol', self.vt_symbol) order_time = order_info['order_time'] order_volume = (order_info['volume'] - order_info['traded']) order_grid = order_info['grid'] order_status = order_info.get('status', Status.NOTTRADED) order_type = order_info.get('order_type', OrderType.LIMIT) over_seconds = (dt - order_time).total_seconds() if ((order_status in [Status.NOTTRADED, Status.SUBMITTING]) and ((order_type == OrderType.LIMIT) or ('.SPD' in order_vt_symbol))): if ((over_seconds > self.cancel_seconds) or force): self.write_log(u' => 超时{}秒未成交,取消委托单:vt_orderid:{},order:{}'.format(over_seconds, vt_orderid, order_info)) order_info.update({'status': Status.CANCELLING}) self.active_orders.update({vt_orderid: order_info}) ret = self.cancel_order(str(vt_orderid)) if (not ret): self.write_error(f'{self.strategy_name} => {order_vt_symbol}撤单失败') continue elif (order_status == Status.CANCELLED): self.write_log(u' => 委托单{}已成功撤单,将删除未完成订单{}'.format(vt_orderid, order_info)) canceled_ids.append(vt_orderid) if reopen: if (order_info['offset'] == Offset.OPEN): self.write_log(u' => 重新开仓') if (order_info['direction'] == Direction.SHORT): short_price = (self.cur_mi_price - self.price_tick) if ((order_grid.volume != order_volume) and (order_volume > 0)): self.write_log(u'网格volume:{},order_volume:{}不一致,修正'.format(order_grid.volume, order_volume)) order_grid.volume = order_volume self.write_log(u'重新提交{}开空委托,开空价{},v:{}'.format(order_vt_symbol, short_price, order_volume)) vt_orderids = self.short(price=short_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderid:{}'.format(vt_orderids)) order_grid.snapshot.update({'open_price': short_price}) else: self.write_error(u'撤单后,重新委托开空仓失败') else: buy_price = (self.cur_mi_price + self.price_tick) if ((order_grid.volume != order_volume) and (order_volume > 0)): self.write_log(u'网格volume:{},order_volume:{}不一致,修正'.format(order_grid.volume, order_volume)) order_grid.volume = order_volume self.write_log(u'重新提交{}开多委托,开多价{},v:{}'.format(order_vt_symbol, buy_price, order_volume)) vt_orderids = self.buy(price=buy_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) order_grid.snapshot.update({'open_price': buy_price}) else: self.write_error(u'撤单后,重新委托开多仓失败') elif (order_info['direction'] == Direction.SHORT): sell_price = (self.cur_mi_price - self.price_tick) self.write_log(u'重新提交{}平多委托,{},v:{}'.format(order_vt_symbol, sell_price, order_volume)) vt_orderids = self.sell(price=sell_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) else: self.write_error(u'撤单后,重新委托平多仓失败') else: cover_price = (self.cur_mi_price + self.price_tick) self.write_log(u'重新提交{}平空委托,委托价{},v:{}'.format(order_vt_symbol, cover_price, order_volume)) vt_orderids = self.cover(price=cover_price, volume=order_volume, vt_symbol=order_vt_symbol, order_type=order_type, order_time=self.cur_datetime, grid=order_grid) if (len(vt_orderids) > 0): self.write_log(u'委托成功,orderids:{}'.format(vt_orderids)) else: self.write_error(u'撤单后,重新委托平空仓失败') else: self.write_log(u' => 无须重新开仓') if ((order_info['offset'] == Offset.OPEN) and order_grid and (len(order_grid.order_ids) == 0)): if ((order_info['traded'] == 0) and (order_grid.traded_volume == 0)): self.write_log(u' => 无任何成交 => 移除委托网格{}'.format(order_grid.__dict__)) order_info['grid'] = None self.gt.remove_grids_by_ids(direction=order_grid.direction, ids=[order_grid.id]) elif (order_info['traded'] > 0): self.write_log(' = > 部分开仓') if (order_grid.traded_volume < order_info['traded']): self.write_log(' = > 调整网格开仓数 {} => {}'.format(order_grid.traded_volume, order_info['traded'])) order_grid.traded_volume = order_info['traded'] self.write_log(f' => 调整网格委托状态=> False, 开仓状态:True, 开仓数量:{order_grid.volume}=>{order_grid.traded_volume}') order_grid.order_status = False order_grid.open_status = True order_grid.volume = order_grid.traded_volume order_grid.traded_volume = 0 for vt_orderid in canceled_ids: self.write_log(u' => 删除未完成订单:{}'.format(vt_orderid)) self.active_orders.pop(vt_orderid, None) if (len(self.active_orders) == 0): self.entrust = 0
def tns_close_long_pos(self, grid): '\n 事务平多单仓位\n 1.来源自止损止盈平仓\n 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格.\n :param 平仓网格\n :return:\n ' self.write_log(u'执行事务平多仓位:{}'.format(grid.to_json())) sell_symbol = grid.snapshot.get('mi_symbol', self.vt_symbol) grid_pos = self.cta_engine.get_position_holding(vt_symbol=sell_symbol) if (grid_pos is None): self.write_error(u'无法获取{}得持仓信息'.format(sell_symbol)) return False if (((grid_pos.long_yd >= grid.volume > 0) and (grid_pos.long_td == 0) and (grid_pos.short_td == 0)) or (not self.activate_today_lock)): if self.activate_today_lock: self.write_log(u'昨仓多单:{},没有今仓,满足条件,直接平昨仓'.format(grid_pos.long_yd)) sell_price = self.cta_engine.get_price(sell_symbol) if (sell_price is None): self.write_error(f'暂时不能获取{sell_symbol}价格,不能平仓') return False if (not self.backtesting): sell_tick = self.cta_engine.get_tick(sell_symbol) if (sell_tick and (0 < sell_tick.bid_price_1 < sell_price)): sell_price = sell_tick.bid_price_1 if (grid.traded_volume > 0): grid.volume -= grid.traded_volume grid.traded_volume = 0 if ((self.exchange != Exchange.CFFEX) and (grid_pos.long_pos < grid.volume)): self.write_error(f'账号{sell_symbol}多单持仓:{grid_pos.long_pos}不满足平仓:{grid.volume}要求:') return False vt_orderids = self.sell(price=sell_price, volume=grid.volume, vt_symbol=sell_symbol, order_type=self.order_type, order_time=self.cur_datetime, lock=(self.exchange == Exchange.CFFEX), grid=grid) if (len(vt_orderids) == 0): self.write_error(u'多单平仓委托失败') return False else: self.write_log(u'多单平仓委托成功,编号:{}'.format(vt_orderids)) return True else: self.write_log(u'昨仓多单:{}不满足条件,创建对锁仓'.format(grid_pos.long_yd)) dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = sell_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = grid.volume dist_record['operation'] = 'add short lock[long]' self.save_dist(dist_record) lock_grid = copy(grid) lock_grid.type = LOCK_GRID lock_grid.id = str(uuid.uuid1()) lock_grid.direction = Direction.SHORT lock_grid.open_status = False lock_grid.order_status = False lock_grid.order_ids = [] vt_orderids = self.short(self.cur_mi_price, volume=lock_grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, order_time=self.cur_datetime, grid=lock_grid) if (len(vt_orderids) > 0): grid.type = LOCK_GRID self.write_log(u'委托创建对锁单(空单)成功,委托编号:{},{},p:{},v:{}'.format(vt_orderids, sell_symbol, self.cur_mi_price, lock_grid.volume)) lock_grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.up_grids.append(lock_grid) return True else: self.write_error(u'未能委托对锁单(空单)') return False
-4,714,678,350,882,631,000
事务平多单仓位 1.来源自止损止盈平仓 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格. :param 平仓网格 :return:
vnpy/app/cta_strategy_pro/template.py
tns_close_long_pos
UtorYeung/vnpy
python
def tns_close_long_pos(self, grid): '\n 事务平多单仓位\n 1.来源自止损止盈平仓\n 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格.\n :param 平仓网格\n :return:\n ' self.write_log(u'执行事务平多仓位:{}'.format(grid.to_json())) sell_symbol = grid.snapshot.get('mi_symbol', self.vt_symbol) grid_pos = self.cta_engine.get_position_holding(vt_symbol=sell_symbol) if (grid_pos is None): self.write_error(u'无法获取{}得持仓信息'.format(sell_symbol)) return False if (((grid_pos.long_yd >= grid.volume > 0) and (grid_pos.long_td == 0) and (grid_pos.short_td == 0)) or (not self.activate_today_lock)): if self.activate_today_lock: self.write_log(u'昨仓多单:{},没有今仓,满足条件,直接平昨仓'.format(grid_pos.long_yd)) sell_price = self.cta_engine.get_price(sell_symbol) if (sell_price is None): self.write_error(f'暂时不能获取{sell_symbol}价格,不能平仓') return False if (not self.backtesting): sell_tick = self.cta_engine.get_tick(sell_symbol) if (sell_tick and (0 < sell_tick.bid_price_1 < sell_price)): sell_price = sell_tick.bid_price_1 if (grid.traded_volume > 0): grid.volume -= grid.traded_volume grid.traded_volume = 0 if ((self.exchange != Exchange.CFFEX) and (grid_pos.long_pos < grid.volume)): self.write_error(f'账号{sell_symbol}多单持仓:{grid_pos.long_pos}不满足平仓:{grid.volume}要求:') return False vt_orderids = self.sell(price=sell_price, volume=grid.volume, vt_symbol=sell_symbol, order_type=self.order_type, order_time=self.cur_datetime, lock=(self.exchange == Exchange.CFFEX), grid=grid) if (len(vt_orderids) == 0): self.write_error(u'多单平仓委托失败') return False else: self.write_log(u'多单平仓委托成功,编号:{}'.format(vt_orderids)) return True else: self.write_log(u'昨仓多单:{}不满足条件,创建对锁仓'.format(grid_pos.long_yd)) dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = sell_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = grid.volume dist_record['operation'] = 'add short lock[long]' self.save_dist(dist_record) lock_grid = copy(grid) lock_grid.type = LOCK_GRID lock_grid.id = str(uuid.uuid1()) lock_grid.direction = Direction.SHORT lock_grid.open_status = False lock_grid.order_status = False lock_grid.order_ids = [] vt_orderids = self.short(self.cur_mi_price, volume=lock_grid.volume, vt_symbol=self.vt_symbol, order_type=self.order_type, order_time=self.cur_datetime, grid=lock_grid) if (len(vt_orderids) > 0): grid.type = LOCK_GRID self.write_log(u'委托创建对锁单(空单)成功,委托编号:{},{},p:{},v:{}'.format(vt_orderids, sell_symbol, self.cur_mi_price, lock_grid.volume)) lock_grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.up_grids.append(lock_grid) return True else: self.write_error(u'未能委托对锁单(空单)') return False
def tns_close_short_pos(self, grid): '\n 事务平空单仓位\n 1.来源自止损止盈平仓\n 2.来源自换仓\n 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格.\n :param 平仓网格\n :return:\n ' self.write_log(u'执行事务平空仓位:{}'.format(grid.to_json())) cover_symbol = grid.snapshot.get('mi_symbol', self.vt_symbol) grid_pos = self.cta_engine.get_position_holding(cover_symbol) if (grid_pos is None): self.write_error(u'无法获取{}得持仓信息'.format(cover_symbol)) return False if (((grid_pos.short_yd >= grid.volume > 0) and (grid_pos.long_td == 0) and (grid_pos.short_td == 0)) or (not self.activate_today_lock)): if self.activate_today_lock: self.write_log(u'昨仓空单:{},没有今仓, 满足条件,直接平昨仓'.format(grid_pos.short_yd)) cover_price = self.cta_engine.get_price(cover_symbol) if (cover_price is None): self.write_error(f'暂时没有{cover_symbol}行情,不能执行平仓') return False if (not self.backtesting): cover_tick = self.cta_engine.get_tick(cover_symbol) if (cover_tick and (0 < cover_price < cover_tick.ask_price_1)): cover_price = cover_tick.ask_price_1 if (grid.traded_volume > 0): grid.volume -= grid.traded_volume grid.traded_volume = 0 if ((self.exchange != Exchange.CFFEX) and (grid_pos.short_pos < grid.volume)): self.write_error(f'账号{cover_symbol}空单持仓:{grid_pos.short_pos}不满足平仓:{grid.volume}要求:') return False vt_orderids = self.cover(price=cover_price, volume=grid.volume, vt_symbol=cover_symbol, order_type=self.order_type, order_time=self.cur_datetime, lock=(self.exchange == Exchange.CFFEX), grid=grid) if (len(vt_orderids) == 0): self.write_error(u'空单平仓委托失败') return False else: self.write_log(u'空单平仓委托成功,编号:{}'.format(vt_orderids)) return True else: self.write_log(u'昨仓空单:{}不满足条件,建立对锁仓'.format(grid_pos.short_yd)) dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = cover_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = grid.volume dist_record['operation'] = 'add long lock[short]' self.save_dist(dist_record) lock_grid = copy(grid) lock_grid.type = LOCK_GRID lock_grid.id = str(uuid.uuid1()) lock_grid.direction = Direction.LONG lock_grid.open_status = False lock_grid.order_status = False lock_grid.order_ids = [] vt_orderids = self.buy(price=self.cur_mi_price, volume=lock_grid.volume, vt_symbol=cover_symbol, order_type=self.order_type, grid=lock_grid) if (len(vt_orderids) > 0): grid.type = LOCK_GRID self.write_log(u'委托创建对锁单(多单)成功,委托编号:{},{},p:{},v:{}'.format(vt_orderids, self.vt_symbol, self.cur_mi_price, lock_grid.volume)) lock_grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.dn_grids.append(lock_grid) return True else: self.write_error(u'未能委托对锁单(多单)') return False
-5,664,663,188,158,522,000
事务平空单仓位 1.来源自止损止盈平仓 2.来源自换仓 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格. :param 平仓网格 :return:
vnpy/app/cta_strategy_pro/template.py
tns_close_short_pos
UtorYeung/vnpy
python
def tns_close_short_pos(self, grid): '\n 事务平空单仓位\n 1.来源自止损止盈平仓\n 2.来源自换仓\n 逻辑: 如果当前账号昨仓满足平仓数量,直接平仓,如果不满足,则创建锁仓网格.\n :param 平仓网格\n :return:\n ' self.write_log(u'执行事务平空仓位:{}'.format(grid.to_json())) cover_symbol = grid.snapshot.get('mi_symbol', self.vt_symbol) grid_pos = self.cta_engine.get_position_holding(cover_symbol) if (grid_pos is None): self.write_error(u'无法获取{}得持仓信息'.format(cover_symbol)) return False if (((grid_pos.short_yd >= grid.volume > 0) and (grid_pos.long_td == 0) and (grid_pos.short_td == 0)) or (not self.activate_today_lock)): if self.activate_today_lock: self.write_log(u'昨仓空单:{},没有今仓, 满足条件,直接平昨仓'.format(grid_pos.short_yd)) cover_price = self.cta_engine.get_price(cover_symbol) if (cover_price is None): self.write_error(f'暂时没有{cover_symbol}行情,不能执行平仓') return False if (not self.backtesting): cover_tick = self.cta_engine.get_tick(cover_symbol) if (cover_tick and (0 < cover_price < cover_tick.ask_price_1)): cover_price = cover_tick.ask_price_1 if (grid.traded_volume > 0): grid.volume -= grid.traded_volume grid.traded_volume = 0 if ((self.exchange != Exchange.CFFEX) and (grid_pos.short_pos < grid.volume)): self.write_error(f'账号{cover_symbol}空单持仓:{grid_pos.short_pos}不满足平仓:{grid.volume}要求:') return False vt_orderids = self.cover(price=cover_price, volume=grid.volume, vt_symbol=cover_symbol, order_type=self.order_type, order_time=self.cur_datetime, lock=(self.exchange == Exchange.CFFEX), grid=grid) if (len(vt_orderids) == 0): self.write_error(u'空单平仓委托失败') return False else: self.write_log(u'空单平仓委托成功,编号:{}'.format(vt_orderids)) return True else: self.write_log(u'昨仓空单:{}不满足条件,建立对锁仓'.format(grid_pos.short_yd)) dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = cover_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = grid.volume dist_record['operation'] = 'add long lock[short]' self.save_dist(dist_record) lock_grid = copy(grid) lock_grid.type = LOCK_GRID lock_grid.id = str(uuid.uuid1()) lock_grid.direction = Direction.LONG lock_grid.open_status = False lock_grid.order_status = False lock_grid.order_ids = [] vt_orderids = self.buy(price=self.cur_mi_price, volume=lock_grid.volume, vt_symbol=cover_symbol, order_type=self.order_type, grid=lock_grid) if (len(vt_orderids) > 0): grid.type = LOCK_GRID self.write_log(u'委托创建对锁单(多单)成功,委托编号:{},{},p:{},v:{}'.format(vt_orderids, self.vt_symbol, self.cur_mi_price, lock_grid.volume)) lock_grid.snapshot.update({'mi_symbol': self.vt_symbol, 'open_price': self.cur_mi_price}) self.gt.dn_grids.append(lock_grid) return True else: self.write_error(u'未能委托对锁单(多单)') return False
def tns_open_from_lock(self, open_symbol, open_volume, grid_type, open_direction): '\n 从锁仓单中,获取已开的网格(对手仓设置为止损)\n 1, 检查多空锁仓单中,是否有满足数量得昨仓,\n 2, 定位到需求网格,\n :param open_symbol: 开仓合约(主力合约)\n :param open_volume:\n :param grid_type 更新网格的类型\n :param open_direction: 开仓方向\n :return: None, 保留的格\n ' locked_long_grids = self.gt.get_opened_grids_within_types(direction=Direction.LONG, types=[LOCK_GRID]) if (len(locked_long_grids) == 0): return None locked_long_dict = {} for g in locked_long_grids: symbol = g.snapshot.get('mi_symbol', self.vt_symbol) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不纳入计算'.format(g.to_json())) continue if (symbol != open_symbol): self.write_log(u'不处理symbol不一致: 委托请求:{}, Grid mi Symbol:{}'.format(open_symbol, symbol)) continue volume = (g.volume - g.traded_volume) locked_long_dict.update({symbol: (locked_long_dict.get(symbol, 0) + volume)}) locked_long_volume = locked_long_dict.get(open_symbol, 0) if (locked_long_volume < open_volume): self.write_log(u'锁单中,没有足够得多单:{},需求:{}'.format(locked_long_volume, open_volume)) return None locked_short_grids = self.gt.get_opened_grids_within_types(direction=Direction.SHORT, types=[LOCK_GRID]) if (len(locked_short_grids) == 0): return None locked_short_dict = {} for g in locked_short_grids: symbol = g.snapshot.get('mi_symbol', self.vt_symbol) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) continue if (symbol != open_symbol): self.write_log(u'不处理symbol不一致: 委托请求:{}, Grid mi Symbol:{}'.format(open_symbol, symbol)) continue volume = (g.volume - g.traded_volume) locked_short_dict.update({symbol: (locked_short_dict.get(symbol, 0) + volume)}) locked_short_volume = locked_short_dict.get(open_symbol, 0) if (locked_short_volume < open_volume): self.write_log(u'锁单中,没有足够得空单:{},需求:{}'.format(locked_short_volume, open_volume)) return None symbol_pos = self.cta_engine.get_position_holding(open_symbol) if (((open_direction == Direction.LONG) and (symbol_pos.short_yd < open_volume)) or ((open_direction == Direction.SHORT) and (symbol_pos.long_yd < open_volume))): self.write_log(u'昨仓数量,多单:{},空单:{},不满足:{}'.format(symbol_pos.long_yd, symbol_pos.short_yd, open_volume)) return None target_long_grid = None remove_long_grid_ids = [] for g in sorted(locked_long_grids, key=(lambda grid: grid.volume)): if (g.order_status or (len(g.order_ids) > 0)): continue if (target_long_grid is None): target_long_grid = g if (g.volume == open_volume): self.write_log(u'第一个网格持仓数量一致:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) break elif (g.volume > open_volume): self.write_log(u'第一个网格持仓数量大于需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) remain_grid = copy(g) g.volume = open_volume remain_grid.volume -= open_volume remain_grid.id = str(uuid.uuid1()) self.gt.dn_grids.append(remain_grid) self.write_log(u'添加剩余仓位到新多单网格:g.volume:{}'.format(remain_grid.volume)) break elif (g.volume <= (open_volume - target_long_grid.volume)): self.write_log(u'网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) target_long_grid.volume += g.volume g.volume = 0 self.write_log(u'计划移除:{}'.format(g.id)) remove_long_grid_ids.append(g.id) else: self.write_log(u'转移前网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) g.volume -= (open_volume - target_long_grid.volume) target_long_grid.volume = open_volume self.write_log(u'转移后网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) break target_short_grid = None remove_short_grid_ids = [] for g in sorted(locked_short_grids, key=(lambda grid: grid.volume)): if (g.order_status or g.order_ids): continue if (target_short_grid is None): target_short_grid = g if (g.volume == open_volume): self.write_log(u'第一个空单网格持仓数量满足需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) break elif (g.volume > open_volume): self.write_log(u'第一个空单网格持仓数量大于需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) remain_grid = copy(g) g.volume = open_volume remain_grid.volume -= open_volume remain_grid.id = str(uuid.uuid1()) self.gt.up_grids.append(remain_grid) self.write_log(u'添加剩余仓位到新空单网格:g.volume:{}'.format(remain_grid.volume)) break elif (g.volume <= (open_volume - target_short_grid.volume)): target_short_grid.volume += g.volume g.volume = 0 remove_short_grid_ids.append(g.id) else: self.write_log(u'转移前空单网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_short_grid.volume)) g.volume -= (open_volume - target_short_grid.volume) target_short_grid.volume = open_volume self.write_log(u'转移后空单网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_short_grid.volume)) break if ((target_long_grid.volume is None) or (target_short_grid is None)): self.write_log(u'未能定位多单网格和空单网格,不能解锁') return None self.gt.remove_grids_by_ids(direction=Direction.LONG, ids=remove_long_grid_ids) self.gt.remove_grids_by_ids(direction=Direction.SHORT, ids=remove_short_grid_ids) if (open_direction == Direction.LONG): self.write_log(u'保留多单,对空单:{}平仓'.format(target_short_grid.id)) cover_price = self.cta_engine.get_price(open_symbol) self.write_log(u'空单止损价 :{} =>{}'.format(target_short_grid.stop_price, (cover_price - (10 * self.price_tick)))) target_short_grid.stop_price = (cover_price - (10 * self.price_tick)) self.write_log(u'空单类型 :{} =>{}'.format(target_short_grid.type, grid_type)) target_short_grid.type = grid_type return target_long_grid else: self.write_log(u'保留空单,对多单平仓') sell_price = self.cta_engine.get_price(open_symbol) self.write_log(u'多单止损价 :{} =>{}'.format(target_short_grid.stop_price, (sell_price + (10 * self.price_tick)))) target_long_grid.stop_price = (sell_price + (10 * self.price_tick)) self.write_log(u'多单类型 :{} =>{}'.format(target_short_grid.type, grid_type)) target_long_grid.type = grid_type return target_short_grid
4,465,916,099,554,728,000
从锁仓单中,获取已开的网格(对手仓设置为止损) 1, 检查多空锁仓单中,是否有满足数量得昨仓, 2, 定位到需求网格, :param open_symbol: 开仓合约(主力合约) :param open_volume: :param grid_type 更新网格的类型 :param open_direction: 开仓方向 :return: None, 保留的格
vnpy/app/cta_strategy_pro/template.py
tns_open_from_lock
UtorYeung/vnpy
python
def tns_open_from_lock(self, open_symbol, open_volume, grid_type, open_direction): '\n 从锁仓单中,获取已开的网格(对手仓设置为止损)\n 1, 检查多空锁仓单中,是否有满足数量得昨仓,\n 2, 定位到需求网格,\n :param open_symbol: 开仓合约(主力合约)\n :param open_volume:\n :param grid_type 更新网格的类型\n :param open_direction: 开仓方向\n :return: None, 保留的格\n ' locked_long_grids = self.gt.get_opened_grids_within_types(direction=Direction.LONG, types=[LOCK_GRID]) if (len(locked_long_grids) == 0): return None locked_long_dict = {} for g in locked_long_grids: symbol = g.snapshot.get('mi_symbol', self.vt_symbol) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不纳入计算'.format(g.to_json())) continue if (symbol != open_symbol): self.write_log(u'不处理symbol不一致: 委托请求:{}, Grid mi Symbol:{}'.format(open_symbol, symbol)) continue volume = (g.volume - g.traded_volume) locked_long_dict.update({symbol: (locked_long_dict.get(symbol, 0) + volume)}) locked_long_volume = locked_long_dict.get(open_symbol, 0) if (locked_long_volume < open_volume): self.write_log(u'锁单中,没有足够得多单:{},需求:{}'.format(locked_long_volume, open_volume)) return None locked_short_grids = self.gt.get_opened_grids_within_types(direction=Direction.SHORT, types=[LOCK_GRID]) if (len(locked_short_grids) == 0): return None locked_short_dict = {} for g in locked_short_grids: symbol = g.snapshot.get('mi_symbol', self.vt_symbol) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) continue if (symbol != open_symbol): self.write_log(u'不处理symbol不一致: 委托请求:{}, Grid mi Symbol:{}'.format(open_symbol, symbol)) continue volume = (g.volume - g.traded_volume) locked_short_dict.update({symbol: (locked_short_dict.get(symbol, 0) + volume)}) locked_short_volume = locked_short_dict.get(open_symbol, 0) if (locked_short_volume < open_volume): self.write_log(u'锁单中,没有足够得空单:{},需求:{}'.format(locked_short_volume, open_volume)) return None symbol_pos = self.cta_engine.get_position_holding(open_symbol) if (((open_direction == Direction.LONG) and (symbol_pos.short_yd < open_volume)) or ((open_direction == Direction.SHORT) and (symbol_pos.long_yd < open_volume))): self.write_log(u'昨仓数量,多单:{},空单:{},不满足:{}'.format(symbol_pos.long_yd, symbol_pos.short_yd, open_volume)) return None target_long_grid = None remove_long_grid_ids = [] for g in sorted(locked_long_grids, key=(lambda grid: grid.volume)): if (g.order_status or (len(g.order_ids) > 0)): continue if (target_long_grid is None): target_long_grid = g if (g.volume == open_volume): self.write_log(u'第一个网格持仓数量一致:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) break elif (g.volume > open_volume): self.write_log(u'第一个网格持仓数量大于需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) remain_grid = copy(g) g.volume = open_volume remain_grid.volume -= open_volume remain_grid.id = str(uuid.uuid1()) self.gt.dn_grids.append(remain_grid) self.write_log(u'添加剩余仓位到新多单网格:g.volume:{}'.format(remain_grid.volume)) break elif (g.volume <= (open_volume - target_long_grid.volume)): self.write_log(u'网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) target_long_grid.volume += g.volume g.volume = 0 self.write_log(u'计划移除:{}'.format(g.id)) remove_long_grid_ids.append(g.id) else: self.write_log(u'转移前网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) g.volume -= (open_volume - target_long_grid.volume) target_long_grid.volume = open_volume self.write_log(u'转移后网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_long_grid.volume)) break target_short_grid = None remove_short_grid_ids = [] for g in sorted(locked_short_grids, key=(lambda grid: grid.volume)): if (g.order_status or g.order_ids): continue if (target_short_grid is None): target_short_grid = g if (g.volume == open_volume): self.write_log(u'第一个空单网格持仓数量满足需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) break elif (g.volume > open_volume): self.write_log(u'第一个空单网格持仓数量大于需求:g.volume:{},open_volume:{}'.format(g.volume, open_volume)) remain_grid = copy(g) g.volume = open_volume remain_grid.volume -= open_volume remain_grid.id = str(uuid.uuid1()) self.gt.up_grids.append(remain_grid) self.write_log(u'添加剩余仓位到新空单网格:g.volume:{}'.format(remain_grid.volume)) break elif (g.volume <= (open_volume - target_short_grid.volume)): target_short_grid.volume += g.volume g.volume = 0 remove_short_grid_ids.append(g.id) else: self.write_log(u'转移前空单网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_short_grid.volume)) g.volume -= (open_volume - target_short_grid.volume) target_short_grid.volume = open_volume self.write_log(u'转移后空单网格持仓数量:g.volume:{},open_volume:{},保留格:{}'.format(g.volume, open_volume, target_short_grid.volume)) break if ((target_long_grid.volume is None) or (target_short_grid is None)): self.write_log(u'未能定位多单网格和空单网格,不能解锁') return None self.gt.remove_grids_by_ids(direction=Direction.LONG, ids=remove_long_grid_ids) self.gt.remove_grids_by_ids(direction=Direction.SHORT, ids=remove_short_grid_ids) if (open_direction == Direction.LONG): self.write_log(u'保留多单,对空单:{}平仓'.format(target_short_grid.id)) cover_price = self.cta_engine.get_price(open_symbol) self.write_log(u'空单止损价 :{} =>{}'.format(target_short_grid.stop_price, (cover_price - (10 * self.price_tick)))) target_short_grid.stop_price = (cover_price - (10 * self.price_tick)) self.write_log(u'空单类型 :{} =>{}'.format(target_short_grid.type, grid_type)) target_short_grid.type = grid_type return target_long_grid else: self.write_log(u'保留空单,对多单平仓') sell_price = self.cta_engine.get_price(open_symbol) self.write_log(u'多单止损价 :{} =>{}'.format(target_short_grid.stop_price, (sell_price + (10 * self.price_tick)))) target_long_grid.stop_price = (sell_price + (10 * self.price_tick)) self.write_log(u'多单类型 :{} =>{}'.format(target_short_grid.type, grid_type)) target_long_grid.type = grid_type return target_short_grid
def tns_close_locked_grids(self, grid_type): '\n 事务对所有对锁网格进行平仓\n :return:\n ' if (self.entrust != 0): return if (not self.activate_today_lock): return locked_long_grids = self.gt.get_opened_grids_within_types(direction=Direction.LONG, types=[LOCK_GRID]) if (len(locked_long_grids) == 0): return locked_long_dict = {} for g in locked_long_grids: vt_symbol = g.snapshot.get('mi_symbol', self.vt_symbol) volume = (g.volume - g.traded_volume) locked_long_dict.update({vt_symbol: (locked_long_dict.get(vt_symbol, 0) + volume)}) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) return locked_long_volume = sum(locked_long_dict.values(), 0) locked_short_grids = self.gt.get_opened_grids_within_types(direction=Direction.SHORT, types=[LOCK_GRID]) if (len(locked_short_grids) == 0): return locked_short_dict = {} for g in locked_short_grids: vt_symbol = g.snapshot.get('mi_symbol', self.vt_symbol) volume = (g.volume - g.traded_volume) locked_short_dict.update({vt_symbol: (locked_short_dict.get(vt_symbol, 0) + volume)}) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) return locked_short_volume = sum(locked_short_dict.values(), 0) self.write_log(u'多单对锁格:{}'.format([g.to_json() for g in locked_long_grids])) self.write_log(u'空单对锁格:{}'.format([g.to_json() for g in locked_short_grids])) if (locked_long_volume != locked_short_volume): self.write_error(u'{}对锁格多空数量不一致,不能解锁.\n多:{},\n空:{}'.format(self.strategy_name, locked_long_volume, locked_short_volume)) return for (vt_symbol, volume) in locked_long_dict.items(): pos = self.cta_engine.get_position_holding(vt_symbol, None) if (pos is None): self.write_error(u'{} 没有获取{}得持仓信息,不能解锁') return if ((pos.long_yd < volume) or (pos.short_yd < volume)): self.write_error(u'{}持仓昨仓多单:{},空单:{},不满足解锁数量:{}'.format(vt_symbol, pos.long_yd, pos.short_td, volume)) return if ((pos.long_td > 0) or (pos.short_td > 0)): self.write_log(u'{}存在今多仓:{},空仓{},不满足解锁条件'.format(vt_symbol, pos.long_td, pos.short_td)) return price = self.cta_engine.get_price(vt_symbol) if (price is None): self.write_error(u'{}价格不在tick_dict缓存中,不能解锁'.format(vt_symbol)) for g in locked_long_grids: dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.vt_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = g.volume dist_record['operation'] = 'close lock[long]' self.save_dist(dist_record) self.write_log(u'网格 从锁仓 {}=>{},提升止损价{}=>{}进行离场'.format(LOCK_GRID, grid_type, g.stop_price, (self.cur_99_price / 2))) g.type = grid_type g.stop_price = (self.cur_99_price / 2) for g in locked_short_grids: dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.vt_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = g.volume dist_record['operation'] = 'close lock[short]' self.save_dist(dist_record) self.write_log(u'网格 从锁仓 {}=>{},提升止损价{}=>{}进行离场'.format(LOCK_GRID, grid_type, g.stop_price, (self.cur_99_price * 2))) g.type = grid_type g.stop_price = (self.cur_99_price * 2)
-4,562,718,120,327,982,000
事务对所有对锁网格进行平仓 :return:
vnpy/app/cta_strategy_pro/template.py
tns_close_locked_grids
UtorYeung/vnpy
python
def tns_close_locked_grids(self, grid_type): '\n 事务对所有对锁网格进行平仓\n :return:\n ' if (self.entrust != 0): return if (not self.activate_today_lock): return locked_long_grids = self.gt.get_opened_grids_within_types(direction=Direction.LONG, types=[LOCK_GRID]) if (len(locked_long_grids) == 0): return locked_long_dict = {} for g in locked_long_grids: vt_symbol = g.snapshot.get('mi_symbol', self.vt_symbol) volume = (g.volume - g.traded_volume) locked_long_dict.update({vt_symbol: (locked_long_dict.get(vt_symbol, 0) + volume)}) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) return locked_long_volume = sum(locked_long_dict.values(), 0) locked_short_grids = self.gt.get_opened_grids_within_types(direction=Direction.SHORT, types=[LOCK_GRID]) if (len(locked_short_grids) == 0): return locked_short_dict = {} for g in locked_short_grids: vt_symbol = g.snapshot.get('mi_symbol', self.vt_symbol) volume = (g.volume - g.traded_volume) locked_short_dict.update({vt_symbol: (locked_short_dict.get(vt_symbol, 0) + volume)}) if (g.order_status or g.order_ids): self.write_log(u'当前对锁格:{}存在委托,不进行解锁'.format(g.to_json())) return locked_short_volume = sum(locked_short_dict.values(), 0) self.write_log(u'多单对锁格:{}'.format([g.to_json() for g in locked_long_grids])) self.write_log(u'空单对锁格:{}'.format([g.to_json() for g in locked_short_grids])) if (locked_long_volume != locked_short_volume): self.write_error(u'{}对锁格多空数量不一致,不能解锁.\n多:{},\n空:{}'.format(self.strategy_name, locked_long_volume, locked_short_volume)) return for (vt_symbol, volume) in locked_long_dict.items(): pos = self.cta_engine.get_position_holding(vt_symbol, None) if (pos is None): self.write_error(u'{} 没有获取{}得持仓信息,不能解锁') return if ((pos.long_yd < volume) or (pos.short_yd < volume)): self.write_error(u'{}持仓昨仓多单:{},空单:{},不满足解锁数量:{}'.format(vt_symbol, pos.long_yd, pos.short_td, volume)) return if ((pos.long_td > 0) or (pos.short_td > 0)): self.write_log(u'{}存在今多仓:{},空仓{},不满足解锁条件'.format(vt_symbol, pos.long_td, pos.short_td)) return price = self.cta_engine.get_price(vt_symbol) if (price is None): self.write_error(u'{}价格不在tick_dict缓存中,不能解锁'.format(vt_symbol)) for g in locked_long_grids: dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.vt_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = g.volume dist_record['operation'] = 'close lock[long]' self.save_dist(dist_record) self.write_log(u'网格 从锁仓 {}=>{},提升止损价{}=>{}进行离场'.format(LOCK_GRID, grid_type, g.stop_price, (self.cur_99_price / 2))) g.type = grid_type g.stop_price = (self.cur_99_price / 2) for g in locked_short_grids: dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.vt_symbol dist_record['price'] = self.cur_mi_price dist_record['volume'] = g.volume dist_record['operation'] = 'close lock[short]' self.save_dist(dist_record) self.write_log(u'网格 从锁仓 {}=>{},提升止损价{}=>{}进行离场'.format(LOCK_GRID, grid_type, g.stop_price, (self.cur_99_price * 2))) g.type = grid_type g.stop_price = (self.cur_99_price * 2)
def grid_check_stop(self): '\n 网格逐一止损/止盈检查 (根据指数价格进行止损止盈)\n :return:\n ' if (self.entrust != 0): return if (not self.trading): if (not self.backtesting): self.write_error(u'当前不允许交易') return long_grids = self.gt.get_opened_grids_without_types(direction=Direction.LONG, types=[LOCK_GRID]) for g in long_grids: if ((g.stop_price > 0) and (g.stop_price > self.cur_99_price) and g.open_status and (not g.order_status)): dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.idx_symbol dist_record['volume'] = g.volume dist_record['price'] = self.cur_99_price dist_record['operation'] = 'stop leave' dist_record['signals'] = '{}<{}'.format(self.cur_99_price, g.stop_price) self.write_log(u'{} 指数价:{} 触发多单止损线{},{}当前价:{}。指数开仓价:{},主力开仓价:{},v:{}'.format(self.cur_datetime, self.cur_99_price, g.stop_price, self.vt_symbol, self.cur_mi_price, g.open_price, g.snapshot.get('open_price'), g.volume)) self.save_dist(dist_record) if self.tns_close_long_pos(g): self.write_log(u'多单止盈/止损委托成功') else: self.write_error(u'多单止损委托失败') short_grids = self.gt.get_opened_grids_without_types(direction=Direction.SHORT, types=[LOCK_GRID]) for g in short_grids: if ((g.stop_price > 0) and (g.stop_price < self.cur_99_price) and g.open_status and (not g.order_status)): dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.idx_symbol dist_record['volume'] = g.volume dist_record['price'] = self.cur_99_price dist_record['operation'] = 'stop leave' dist_record['signals'] = '{}<{}'.format(self.cur_99_price, g.stop_price) self.write_log(u'{} 指数价:{} 触发空单止损线:{},{}最新价:{}。指数开仓价:{},主力开仓价:{},v:{}'.format(self.cur_datetime, self.cur_99_price, g.stop_price, self.vt_symbol, self.cur_mi_price, g.open_price, g.snapshot.get('open_price'), g.volume)) self.save_dist(dist_record) if self.tns_close_short_pos(g): self.write_log(u'空单止盈/止损委托成功') else: self.write_error(u'委托空单平仓失败')
-1,047,088,386,559,161,900
网格逐一止损/止盈检查 (根据指数价格进行止损止盈) :return:
vnpy/app/cta_strategy_pro/template.py
grid_check_stop
UtorYeung/vnpy
python
def grid_check_stop(self): '\n 网格逐一止损/止盈检查 (根据指数价格进行止损止盈)\n :return:\n ' if (self.entrust != 0): return if (not self.trading): if (not self.backtesting): self.write_error(u'当前不允许交易') return long_grids = self.gt.get_opened_grids_without_types(direction=Direction.LONG, types=[LOCK_GRID]) for g in long_grids: if ((g.stop_price > 0) and (g.stop_price > self.cur_99_price) and g.open_status and (not g.order_status)): dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.idx_symbol dist_record['volume'] = g.volume dist_record['price'] = self.cur_99_price dist_record['operation'] = 'stop leave' dist_record['signals'] = '{}<{}'.format(self.cur_99_price, g.stop_price) self.write_log(u'{} 指数价:{} 触发多单止损线{},{}当前价:{}。指数开仓价:{},主力开仓价:{},v:{}'.format(self.cur_datetime, self.cur_99_price, g.stop_price, self.vt_symbol, self.cur_mi_price, g.open_price, g.snapshot.get('open_price'), g.volume)) self.save_dist(dist_record) if self.tns_close_long_pos(g): self.write_log(u'多单止盈/止损委托成功') else: self.write_error(u'多单止损委托失败') short_grids = self.gt.get_opened_grids_without_types(direction=Direction.SHORT, types=[LOCK_GRID]) for g in short_grids: if ((g.stop_price > 0) and (g.stop_price < self.cur_99_price) and g.open_status and (not g.order_status)): dist_record = dict() dist_record['datetime'] = self.cur_datetime dist_record['symbol'] = self.idx_symbol dist_record['volume'] = g.volume dist_record['price'] = self.cur_99_price dist_record['operation'] = 'stop leave' dist_record['signals'] = '{}<{}'.format(self.cur_99_price, g.stop_price) self.write_log(u'{} 指数价:{} 触发空单止损线:{},{}最新价:{}。指数开仓价:{},主力开仓价:{},v:{}'.format(self.cur_datetime, self.cur_99_price, g.stop_price, self.vt_symbol, self.cur_mi_price, g.open_price, g.snapshot.get('open_price'), g.volume)) self.save_dist(dist_record) if self.tns_close_short_pos(g): self.write_log(u'空单止盈/止损委托成功') else: self.write_error(u'委托空单平仓失败')
def logando_notification(tipo, mensagem): "\n Generates the log message/Gera a mensagem de log.\n\n :param tipo: Sets the log type/Seta o tipo de log.\n :param mensagem: Sets the message of log/Seta a mensagem do log.\n :return: Returns the complete log's body/Retorna o corpo completo do log.\n " logger = logging.getLogger(__name__) coloredlogs.install(level='DEBUG') coloredlogs.install(level='DEBUG', logger=logger) logging.basicConfig(format='%(asctime)s %(hostname)s %(name)s[%(process)d] %(levelname)s %(message)s') logger = verboselogs.VerboseLogger('') if (tipo == 'verbose'): logger.verbose(mensagem) elif (tipo == 'debug'): logger.debug(mensagem) elif (tipo == 'info'): logger.info(mensagem) elif (tipo == 'warning'): logger.warning(mensagem) elif (tipo == 'error'): logger.error(mensagem) elif (tipo == 'critical'): logger.critical(mensagem) else: pass
2,179,398,016,320,366,000
Generates the log message/Gera a mensagem de log. :param tipo: Sets the log type/Seta o tipo de log. :param mensagem: Sets the message of log/Seta a mensagem do log. :return: Returns the complete log's body/Retorna o corpo completo do log.
Linux/etc/notification/telegram.py
logando_notification
4jinetes/Oblivion
python
def logando_notification(tipo, mensagem): "\n Generates the log message/Gera a mensagem de log.\n\n :param tipo: Sets the log type/Seta o tipo de log.\n :param mensagem: Sets the message of log/Seta a mensagem do log.\n :return: Returns the complete log's body/Retorna o corpo completo do log.\n " logger = logging.getLogger(__name__) coloredlogs.install(level='DEBUG') coloredlogs.install(level='DEBUG', logger=logger) logging.basicConfig(format='%(asctime)s %(hostname)s %(name)s[%(process)d] %(levelname)s %(message)s') logger = verboselogs.VerboseLogger() if (tipo == 'verbose'): logger.verbose(mensagem) elif (tipo == 'debug'): logger.debug(mensagem) elif (tipo == 'info'): logger.info(mensagem) elif (tipo == 'warning'): logger.warning(mensagem) elif (tipo == 'error'): logger.error(mensagem) elif (tipo == 'critical'): logger.critical(mensagem) else: pass
def notificar_telegram(status_nosafe=False, data_nosafe=None): '\n Generates the notification to Telegram account/Gera a notificação para a conta do Telegram.\n ' usuarios = [] with open(f'{path_tl_final}/etc/notification/users.txt', 'r') as lista: separar = lista.readlines() if status_nosafe: mensagem = str(data_nosafe) else: with open(f'{path_tl_final}/etc/notification/message.txt', 'r') as mensagem_corpo: mensagem = str(mensagem_corpo.read()) for i in separar: i = i.strip('\r') i = i.strip('\n') i = i.split(';') usuarios += i for i in usuarios: if ((i == '') or (i == ' ')): usuarios.remove(i) for mandar in usuarios: token = telegram_bot chat_id = mandar texto = mensagem send_message(chat_id=mandar, text=mensagem, token=telegram_bot)
-4,091,007,493,826,592,000
Generates the notification to Telegram account/Gera a notificação para a conta do Telegram.
Linux/etc/notification/telegram.py
notificar_telegram
4jinetes/Oblivion
python
def notificar_telegram(status_nosafe=False, data_nosafe=None): '\n \n ' usuarios = [] with open(f'{path_tl_final}/etc/notification/users.txt', 'r') as lista: separar = lista.readlines() if status_nosafe: mensagem = str(data_nosafe) else: with open(f'{path_tl_final}/etc/notification/message.txt', 'r') as mensagem_corpo: mensagem = str(mensagem_corpo.read()) for i in separar: i = i.strip('\r') i = i.strip('\n') i = i.split(';') usuarios += i for i in usuarios: if ((i == ) or (i == ' ')): usuarios.remove(i) for mandar in usuarios: token = telegram_bot chat_id = mandar texto = mensagem send_message(chat_id=mandar, text=mensagem, token=telegram_bot)
def send_message(chat_id, text=None, parse_mode='Markdown', token=None): '\n Sends message in bold mode/Enviar mensagem em negrito.\n\n :param chat_id: ID of Telegram account/ID da conta Telgram.\n :param text: Message/Mensagem.\n :param parse_mode: Ignore.\n :param token: ID Telegram bot/ID do bot Telegram.\n ' URL = f'https://api.telegram.org/bot{token}/sendMessage?chat_id={chat_id}&text={text}' answer = {'chat_id': chat_id, 'text': text, 'parse_mode': 'Markdown'} r = requests.post(URL, json=answer) if (text == '/bold'): send_message(chat_id, (((('Here comes the' + '*') + 'bold') + '*') + 'text!'))
5,812,060,372,968,654,000
Sends message in bold mode/Enviar mensagem em negrito. :param chat_id: ID of Telegram account/ID da conta Telgram. :param text: Message/Mensagem. :param parse_mode: Ignore. :param token: ID Telegram bot/ID do bot Telegram.
Linux/etc/notification/telegram.py
send_message
4jinetes/Oblivion
python
def send_message(chat_id, text=None, parse_mode='Markdown', token=None): '\n Sends message in bold mode/Enviar mensagem em negrito.\n\n :param chat_id: ID of Telegram account/ID da conta Telgram.\n :param text: Message/Mensagem.\n :param parse_mode: Ignore.\n :param token: ID Telegram bot/ID do bot Telegram.\n ' URL = f'https://api.telegram.org/bot{token}/sendMessage?chat_id={chat_id}&text={text}' answer = {'chat_id': chat_id, 'text': text, 'parse_mode': 'Markdown'} r = requests.post(URL, json=answer) if (text == '/bold'): send_message(chat_id, (((('Here comes the' + '*') + 'bold') + '*') + 'text!'))
async def _on_input(self, command, seat): 'Switch input functionality\n\n Calls on and off depending on command state\n :param command: Command from game engine\n :type command: dict\n :param seat: Robot seat\n :type seat: int\n ' if ('state' not in command): logging.warning('Switch: invalid command received') return try: val = str(command['state']) except (ValueError, TypeError): logging.warn(f"Switch: could not convert {command['state']} into String") return if ((val != 'up') and (val != 'down')): logging.warn('Switch: command not <up|down>') return if (val == 'up'): (await self.off(seat)) else: (await self.on(seat))
-7,261,439,614,335,508,000
Switch input functionality Calls on and off depending on command state :param command: Command from game engine :type command: dict :param seat: Robot seat :type seat: int
surrortg/inputs/switch.py
_on_input
SurrogateInc/surrortg-sdk
python
async def _on_input(self, command, seat): 'Switch input functionality\n\n Calls on and off depending on command state\n :param command: Command from game engine\n :type command: dict\n :param seat: Robot seat\n :type seat: int\n ' if ('state' not in command): logging.warning('Switch: invalid command received') return try: val = str(command['state']) except (ValueError, TypeError): logging.warn(f"Switch: could not convert {command['state']} into String") return if ((val != 'up') and (val != 'down')): logging.warn('Switch: command not <up|down>') return if (val == 'up'): (await self.off(seat)) else: (await self.on(seat))
@abstractmethod async def on(self, seat): 'Switch turned on functionality\n\n :param seat: Robot seat\n :type seat: int\n ' pass
-6,835,234,047,209,246,000
Switch turned on functionality :param seat: Robot seat :type seat: int
surrortg/inputs/switch.py
on
SurrogateInc/surrortg-sdk
python
@abstractmethod async def on(self, seat): 'Switch turned on functionality\n\n :param seat: Robot seat\n :type seat: int\n ' pass
@abstractmethod async def off(self, seat): 'Switch turned off functionality\n\n :param seat: Robot seat\n :type seat: int\n ' pass
-2,710,125,614,147,228,700
Switch turned off functionality :param seat: Robot seat :type seat: int
surrortg/inputs/switch.py
off
SurrogateInc/surrortg-sdk
python
@abstractmethod async def off(self, seat): 'Switch turned off functionality\n\n :param seat: Robot seat\n :type seat: int\n ' pass
async def reset(self, seat): 'Switch reset functionality\n\n Defaults to calling off()\n\n :param seat: Robot seat\n :type seat: int\n ' (await self.off(seat))
-6,922,977,932,009,814,000
Switch reset functionality Defaults to calling off() :param seat: Robot seat :type seat: int
surrortg/inputs/switch.py
reset
SurrogateInc/surrortg-sdk
python
async def reset(self, seat): 'Switch reset functionality\n\n Defaults to calling off()\n\n :param seat: Robot seat\n :type seat: int\n ' (await self.off(seat))
def get_name(self): 'Returns the name of the input\n\n :return: name of the input\n :rtype: str\n ' return 'button'
3,793,174,537,542,569,500
Returns the name of the input :return: name of the input :rtype: str
surrortg/inputs/switch.py
get_name
SurrogateInc/surrortg-sdk
python
def get_name(self): 'Returns the name of the input\n\n :return: name of the input\n :rtype: str\n ' return 'button'
def get_default_keybinds(self): 'Returns a single keybind or a list of keybinds.\n\n Switches are bound to the space key by default.\n\n To override the defaults, override this method in your switch\n subclass and return different keybinds.\n ' return []
-132,505,091,344,185,620
Returns a single keybind or a list of keybinds. Switches are bound to the space key by default. To override the defaults, override this method in your switch subclass and return different keybinds.
surrortg/inputs/switch.py
get_default_keybinds
SurrogateInc/surrortg-sdk
python
def get_default_keybinds(self): 'Returns a single keybind or a list of keybinds.\n\n Switches are bound to the space key by default.\n\n To override the defaults, override this method in your switch\n subclass and return different keybinds.\n ' return []
@classmethod def _cnn_net(cls): '\n Create the CNN net topology.\n :return keras.Sequential(): CNN topology.\n ' qrs_detector = keras.Sequential() qrs_detector.add(keras.layers.Conv1D(96, 49, activation=tf.nn.relu, input_shape=(300, 1), strides=1, name='conv1')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool1')) qrs_detector.add(keras.layers.Conv1D(128, 25, activation=tf.nn.relu, strides=1, name='conv2')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool2')) qrs_detector.add(keras.layers.Conv1D(256, 9, activation=tf.nn.relu, strides=1, name='conv3')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool3')) qrs_detector.add(keras.layers.Conv1D(512, 9, activation=tf.nn.relu, strides=1, name='conv4')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool4')) qrs_detector.add(keras.layers.Flatten(data_format=None, name='flatten')) qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc1')) qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc2')) qrs_detector.add(keras.layers.Dropout(rate=0.5, name='drop1')) qrs_detector.add(keras.layers.Dense(units=2, name='classes')) qrs_detector.add(keras.layers.Activation(activation=tf.nn.softmax, name='softmax')) return qrs_detector
4,818,998,698,767,064,000
Create the CNN net topology. :return keras.Sequential(): CNN topology.
python/qrs/qrs_net.py
_cnn_net
ufopcsilab/ECGClassification
python
@classmethod def _cnn_net(cls): '\n Create the CNN net topology.\n :return keras.Sequential(): CNN topology.\n ' qrs_detector = keras.Sequential() qrs_detector.add(keras.layers.Conv1D(96, 49, activation=tf.nn.relu, input_shape=(300, 1), strides=1, name='conv1')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool1')) qrs_detector.add(keras.layers.Conv1D(128, 25, activation=tf.nn.relu, strides=1, name='conv2')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool2')) qrs_detector.add(keras.layers.Conv1D(256, 9, activation=tf.nn.relu, strides=1, name='conv3')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool3')) qrs_detector.add(keras.layers.Conv1D(512, 9, activation=tf.nn.relu, strides=1, name='conv4')) qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool4')) qrs_detector.add(keras.layers.Flatten(data_format=None, name='flatten')) qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc1')) qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc2')) qrs_detector.add(keras.layers.Dropout(rate=0.5, name='drop1')) qrs_detector.add(keras.layers.Dense(units=2, name='classes')) qrs_detector.add(keras.layers.Activation(activation=tf.nn.softmax, name='softmax')) return qrs_detector
@classmethod def build(cls, net_type): '\n Build the CNN topology.\n :param str net_type: the network type, CNN or LSTM.\n :return keras.Sequential(): CNN topology.\n ' if (net_type == 'cnn'): qrs_detector = cls._cnn_net() else: raise NotImplementedError('Only the CNN network was implemented.') return qrs_detector
-4,566,898,852,637,497,300
Build the CNN topology. :param str net_type: the network type, CNN or LSTM. :return keras.Sequential(): CNN topology.
python/qrs/qrs_net.py
build
ufopcsilab/ECGClassification
python
@classmethod def build(cls, net_type): '\n Build the CNN topology.\n :param str net_type: the network type, CNN or LSTM.\n :return keras.Sequential(): CNN topology.\n ' if (net_type == 'cnn'): qrs_detector = cls._cnn_net() else: raise NotImplementedError('Only the CNN network was implemented.') return qrs_detector
@classmethod def _prepare_data(cls, data_x, input_shape, data_y, number_of_classes, normalize): '\n Prepare the data for the training, turning it into a numpy array.\n :param list data_x: data that will be used to train.\n :param tuple input_shape: the input shape that the data must have to be used as training data.\n :param list data_y: the labels related to the data used to train.\n :param int number_of_classes: number of classes of the problem.\n :param bool normalize: if the data should be normalized (True) or not (False).\n :return np.array: the data processed.\n ' if (len(input_shape) == 2): data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1]) elif (len(input_shape) == 3): data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1], input_shape[2]) else: raise Exception('Only inputs of two and three dimensions were implemented.') if normalize: data_x = (data_x / np.amax(data_x)) data_y = keras.utils.to_categorical(data_y).reshape((- 1), number_of_classes) return (data_x, data_y)
-6,894,264,797,384,032,000
Prepare the data for the training, turning it into a numpy array. :param list data_x: data that will be used to train. :param tuple input_shape: the input shape that the data must have to be used as training data. :param list data_y: the labels related to the data used to train. :param int number_of_classes: number of classes of the problem. :param bool normalize: if the data should be normalized (True) or not (False). :return np.array: the data processed.
python/qrs/qrs_net.py
_prepare_data
ufopcsilab/ECGClassification
python
@classmethod def _prepare_data(cls, data_x, input_shape, data_y, number_of_classes, normalize): '\n Prepare the data for the training, turning it into a numpy array.\n :param list data_x: data that will be used to train.\n :param tuple input_shape: the input shape that the data must have to be used as training data.\n :param list data_y: the labels related to the data used to train.\n :param int number_of_classes: number of classes of the problem.\n :param bool normalize: if the data should be normalized (True) or not (False).\n :return np.array: the data processed.\n ' if (len(input_shape) == 2): data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1]) elif (len(input_shape) == 3): data_x = np.asarray(data_x).reshape((- 1), input_shape[0], input_shape[1], input_shape[2]) else: raise Exception('Only inputs of two and three dimensions were implemented.') if normalize: data_x = (data_x / np.amax(data_x)) data_y = keras.utils.to_categorical(data_y).reshape((- 1), number_of_classes) return (data_x, data_y)
@classmethod def train(cls, model, train_x, train_y, validation_x, validation_y, number_of_classes, input_shape=(300, 1), epochs=10, lr=0.0001, batch_size=4, optimizer=None, loss=None, metrics=None, normalize=False, show_net_info=True): '\n Function used to train the model.\n :param keras.Sequential model: model to be trained.\n :param list train_x: data that will be used to train.\n :param list train_y: the labels related to the data used to train.\n :param list validation_x: data that will be used to validate the model trained.\n :param list validation_y: the labels related to the data used to validate the model trained.\n :param int number_of_classes: number of classes of the problem.\n :param tuple input_shape: the input shape that the data must have to be used as training data.\n :param int epochs: total epochs that the model will be trained.\n :param float lr: learning rate used to train.\n :param int batch_size: batch size used to train.\n :param optimizer: which optimizer will be used to train.\n :param str loss: loss function used during the training.\n :param list metrics: metrics used to evaluate the trained model.\n :param bool normalize: if the data should be normalized (True) or not (False).\n :param bool show_net_info: if the network topology should be showed (True) or not (False).\n :return keras.Sequential, dict: model trained and the history of the training process.\n ' if (optimizer is None): optimizer = keras.optimizers.SGD(lr=lr, momentum=0.9, decay=(0.0001 / epochs)) if (loss is None): loss = keras.losses.categorical_crossentropy if (metrics is None): metrics = ['acc'] elif (type(metrics) is not list): metrics = [metrics] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) if show_net_info: print(model.summary()) (train_x, train_y) = cls._prepare_data(train_x, input_shape, train_y, number_of_classes, normalize) (validation_x, validation_y) = cls._prepare_data(validation_x, input_shape, validation_y, number_of_classes, normalize) kback.set_value(model.optimizer.lr, lr) train_history = model.fit(x=train_x, y=train_y, validation_data=(validation_x, validation_y), batch_size=batch_size, epochs=epochs) return (model, train_history)
-7,580,206,974,420,679,000
Function used to train the model. :param keras.Sequential model: model to be trained. :param list train_x: data that will be used to train. :param list train_y: the labels related to the data used to train. :param list validation_x: data that will be used to validate the model trained. :param list validation_y: the labels related to the data used to validate the model trained. :param int number_of_classes: number of classes of the problem. :param tuple input_shape: the input shape that the data must have to be used as training data. :param int epochs: total epochs that the model will be trained. :param float lr: learning rate used to train. :param int batch_size: batch size used to train. :param optimizer: which optimizer will be used to train. :param str loss: loss function used during the training. :param list metrics: metrics used to evaluate the trained model. :param bool normalize: if the data should be normalized (True) or not (False). :param bool show_net_info: if the network topology should be showed (True) or not (False). :return keras.Sequential, dict: model trained and the history of the training process.
python/qrs/qrs_net.py
train
ufopcsilab/ECGClassification
python
@classmethod def train(cls, model, train_x, train_y, validation_x, validation_y, number_of_classes, input_shape=(300, 1), epochs=10, lr=0.0001, batch_size=4, optimizer=None, loss=None, metrics=None, normalize=False, show_net_info=True): '\n Function used to train the model.\n :param keras.Sequential model: model to be trained.\n :param list train_x: data that will be used to train.\n :param list train_y: the labels related to the data used to train.\n :param list validation_x: data that will be used to validate the model trained.\n :param list validation_y: the labels related to the data used to validate the model trained.\n :param int number_of_classes: number of classes of the problem.\n :param tuple input_shape: the input shape that the data must have to be used as training data.\n :param int epochs: total epochs that the model will be trained.\n :param float lr: learning rate used to train.\n :param int batch_size: batch size used to train.\n :param optimizer: which optimizer will be used to train.\n :param str loss: loss function used during the training.\n :param list metrics: metrics used to evaluate the trained model.\n :param bool normalize: if the data should be normalized (True) or not (False).\n :param bool show_net_info: if the network topology should be showed (True) or not (False).\n :return keras.Sequential, dict: model trained and the history of the training process.\n ' if (optimizer is None): optimizer = keras.optimizers.SGD(lr=lr, momentum=0.9, decay=(0.0001 / epochs)) if (loss is None): loss = keras.losses.categorical_crossentropy if (metrics is None): metrics = ['acc'] elif (type(metrics) is not list): metrics = [metrics] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) if show_net_info: print(model.summary()) (train_x, train_y) = cls._prepare_data(train_x, input_shape, train_y, number_of_classes, normalize) (validation_x, validation_y) = cls._prepare_data(validation_x, input_shape, validation_y, number_of_classes, normalize) kback.set_value(model.optimizer.lr, lr) train_history = model.fit(x=train_x, y=train_y, validation_data=(validation_x, validation_y), batch_size=batch_size, epochs=epochs) return (model, train_history)
def _convert_dataset_to_ground_truth(self, dataset_bboxes): '\n @param `dataset_bboxes`: [[b_x, b_y, b_w, b_h, class_id], ...]\n\n @return `groud_truth_one`:\n [Dim(yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n ' return _convert_dataset_to_ground_truth(dataset_bboxes, self._metayolos_np, self._anchors_np)
5,234,496,081,387,635,000
@param `dataset_bboxes`: [[b_x, b_y, b_w, b_h, class_id], ...] @return `groud_truth_one`: [Dim(yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)
py_src/yolov4/tf/dataset/keras_sequence.py
_convert_dataset_to_ground_truth
fcakyon/tensorflow-yolov4
python
def _convert_dataset_to_ground_truth(self, dataset_bboxes): '\n @param `dataset_bboxes`: [[b_x, b_y, b_w, b_h, class_id], ...]\n\n @return `groud_truth_one`:\n [Dim(yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n ' return _convert_dataset_to_ground_truth(dataset_bboxes, self._metayolos_np, self._anchors_np)
def _convert_dataset_to_image_and_bboxes(self, dataset): '\n @param dataset: [image_path, [[x, y, w, h, class_id], ...]]\n\n @return image, bboxes\n image: 0.0 ~ 1.0, Dim(1, height, width, channels)\n ' try: image = cv2.imread(dataset[0]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) except: return (None, None) (resized_image, resized_bboxes) = media.resize_image(image, target_shape=self._metanet.input_shape, ground_truth=dataset[1]) resized_image = np.expand_dims((resized_image / 255.0), axis=0) return (resized_image, resized_bboxes)
-5,174,543,971,841,476,000
@param dataset: [image_path, [[x, y, w, h, class_id], ...]] @return image, bboxes image: 0.0 ~ 1.0, Dim(1, height, width, channels)
py_src/yolov4/tf/dataset/keras_sequence.py
_convert_dataset_to_image_and_bboxes
fcakyon/tensorflow-yolov4
python
def _convert_dataset_to_image_and_bboxes(self, dataset): '\n @param dataset: [image_path, [[x, y, w, h, class_id], ...]]\n\n @return image, bboxes\n image: 0.0 ~ 1.0, Dim(1, height, width, channels)\n ' try: image = cv2.imread(dataset[0]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) except: return (None, None) (resized_image, resized_bboxes) = media.resize_image(image, target_shape=self._metanet.input_shape, ground_truth=dataset[1]) resized_image = np.expand_dims((resized_image / 255.0), axis=0) return (resized_image, resized_bboxes)
def __getitem__(self, index): '\n @return\n `images`: Dim(batch, height, width, channels)\n `groud_truth_one`:\n [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n ' batch_x = [] batch_y = [[] for _ in range(len(self._metayolos))] start_index = (index * self._metanet.batch) for i in range((self._metanet.batch - self._augmentation_batch)): (image, bboxes) = self._get_dataset((start_index + i)) self._augmentation_cache[self._augmentation_cache_index] = (image, bboxes) self._augmentation_cache_index = ((self._augmentation_cache_index + 1) % _AUGMETATION_CACHE_SIZE) batch_x.append(image) ground_truth = self._convert_dataset_to_ground_truth(bboxes) for j in range(len(self._metayolos)): batch_y[j].append(ground_truth[j]) for i in range(self._augmentation_batch): augmentation = self._augmentation[np.random.randint(0, len(self._augmentation))] image = None bboxes = None if (augmentation == 'mosaic'): (image, bboxes) = mosaic(*[self._augmentation_cache[np.random.randint(0, _AUGMETATION_CACHE_SIZE)] for _ in range(4)]) batch_x.append(image) ground_truth = self._convert_dataset_to_ground_truth(bboxes) for j in range(len(self._metayolos)): batch_y[j].append(ground_truth[j]) return (np.concatenate(batch_x, axis=0), [np.stack(y, axis=0) for y in batch_y])
-8,114,848,881,290,818,000
@return `images`: Dim(batch, height, width, channels) `groud_truth_one`: [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)
py_src/yolov4/tf/dataset/keras_sequence.py
__getitem__
fcakyon/tensorflow-yolov4
python
def __getitem__(self, index): '\n @return\n `images`: Dim(batch, height, width, channels)\n `groud_truth_one`:\n [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n ' batch_x = [] batch_y = [[] for _ in range(len(self._metayolos))] start_index = (index * self._metanet.batch) for i in range((self._metanet.batch - self._augmentation_batch)): (image, bboxes) = self._get_dataset((start_index + i)) self._augmentation_cache[self._augmentation_cache_index] = (image, bboxes) self._augmentation_cache_index = ((self._augmentation_cache_index + 1) % _AUGMETATION_CACHE_SIZE) batch_x.append(image) ground_truth = self._convert_dataset_to_ground_truth(bboxes) for j in range(len(self._metayolos)): batch_y[j].append(ground_truth[j]) for i in range(self._augmentation_batch): augmentation = self._augmentation[np.random.randint(0, len(self._augmentation))] image = None bboxes = None if (augmentation == 'mosaic'): (image, bboxes) = mosaic(*[self._augmentation_cache[np.random.randint(0, _AUGMETATION_CACHE_SIZE)] for _ in range(4)]) batch_x.append(image) ground_truth = self._convert_dataset_to_ground_truth(bboxes) for j in range(len(self._metayolos)): batch_y[j].append(ground_truth[j]) return (np.concatenate(batch_x, axis=0), [np.stack(y, axis=0) for y in batch_y])
def setup_logging(name, dir=''): '\n Setup the logging device to log into a uniquely created directory.\n\n Args:\n name: Name of the directory for the log-files.\n dir: Optional sub-directory within log\n ' global log_name log_name = name global log_dir log_dir = os.path.join('log', dir) if (not os.path.isdir(log_dir)): os.makedirs(log_dir) reload(logging) logging.basicConfig(level=logging.INFO, format='[%(levelname)-5.5s %(asctime)s] %(message)s', datefmt='%H:%M:%S', handlers=[logging.FileHandler(os.path.join(log_dir, (((time.strftime('%Y%m%d_%H%M') + '_') + name) + '.log'))), logging.StreamHandler()])
-7,427,697,294,495,630,000
Setup the logging device to log into a uniquely created directory. Args: name: Name of the directory for the log-files. dir: Optional sub-directory within log
lib/config.py
setup_logging
SudeepSarkar/equilibrium-propagation
python
def setup_logging(name, dir=): '\n Setup the logging device to log into a uniquely created directory.\n\n Args:\n name: Name of the directory for the log-files.\n dir: Optional sub-directory within log\n ' global log_name log_name = name global log_dir log_dir = os.path.join('log', dir) if (not os.path.isdir(log_dir)): os.makedirs(log_dir) reload(logging) logging.basicConfig(level=logging.INFO, format='[%(levelname)-5.5s %(asctime)s] %(message)s', datefmt='%H:%M:%S', handlers=[logging.FileHandler(os.path.join(log_dir, (((time.strftime('%Y%m%d_%H%M') + '_') + name) + '.log'))), logging.StreamHandler()])
def __init__(self, lf, lr, mass, Iz, Cf, Cr, Bf=None, Br=None, Df=None, Dr=None, Cm1=None, Cm2=None, Cr0=None, Cr2=None, input_acc=False, **kwargs): '\tspecify model params here\n\t\t' self.lf = lf self.lr = lr self.dr = (lr / (lf + lr)) self.mass = mass self.Iz = Iz self.Cf = Cf self.Cr = Cr self.Bf = Bf self.Br = Br self.Df = Df self.Dr = Dr self.Cm1 = Cm1 self.Cm2 = Cm2 self.Cr0 = Cr0 self.Cr2 = Cr2 self.approx = False if ((Bf is None) or (Br is None) or (Df is None) or (Dr is None)): self.approx = True self.input_acc = input_acc self.n_states = 6 self.n_inputs = 2 Model.__init__(self)
-3,487,673,847,780,308,500
specify model params here
bayes_race/models/dynamic.py
__init__
KlrShaK/bayesrace
python
def __init__(self, lf, lr, mass, Iz, Cf, Cr, Bf=None, Br=None, Df=None, Dr=None, Cm1=None, Cm2=None, Cr0=None, Cr2=None, input_acc=False, **kwargs): '\t\n\t\t' self.lf = lf self.lr = lr self.dr = (lr / (lf + lr)) self.mass = mass self.Iz = Iz self.Cf = Cf self.Cr = Cr self.Bf = Bf self.Br = Br self.Df = Df self.Dr = Dr self.Cm1 = Cm1 self.Cm2 = Cm2 self.Cr0 = Cr0 self.Cr2 = Cr2 self.approx = False if ((Bf is None) or (Br is None) or (Df is None) or (Dr is None)): self.approx = True self.input_acc = input_acc self.n_states = 6 self.n_inputs = 2 Model.__init__(self)
def sim_continuous(self, x0, u, t): '\tsimulates the nonlinear continuous model with given input vector\n\t\t\tby numerical integration using 6th order Runge Kutta method\n\t\t\tx0 is the initial state of size 6x1\n\t\t\tu is the input vector of size 2xn\n\t\t\tt is the time vector of size 1x(n+1)\n\t\t' n_steps = u.shape[1] x = np.zeros([6, (n_steps + 1)]) dxdt = np.zeros([6, (n_steps + 1)]) dxdt[:, 0] = self._diffequation(None, x0, [0, 0]) x[:, 0] = x0 for ids in range(1, (n_steps + 1)): x[:, ids] = self._integrate(x[:, (ids - 1)], u[:, (ids - 1)], t[(ids - 1)], t[ids]) dxdt[:, ids] = self._diffequation(None, x[:, ids], u[:, (ids - 1)]) return (x, dxdt)
8,414,995,335,463,568,000
simulates the nonlinear continuous model with given input vector by numerical integration using 6th order Runge Kutta method x0 is the initial state of size 6x1 u is the input vector of size 2xn t is the time vector of size 1x(n+1)
bayes_race/models/dynamic.py
sim_continuous
KlrShaK/bayesrace
python
def sim_continuous(self, x0, u, t): '\tsimulates the nonlinear continuous model with given input vector\n\t\t\tby numerical integration using 6th order Runge Kutta method\n\t\t\tx0 is the initial state of size 6x1\n\t\t\tu is the input vector of size 2xn\n\t\t\tt is the time vector of size 1x(n+1)\n\t\t' n_steps = u.shape[1] x = np.zeros([6, (n_steps + 1)]) dxdt = np.zeros([6, (n_steps + 1)]) dxdt[:, 0] = self._diffequation(None, x0, [0, 0]) x[:, 0] = x0 for ids in range(1, (n_steps + 1)): x[:, ids] = self._integrate(x[:, (ids - 1)], u[:, (ids - 1)], t[(ids - 1)], t[ids]) dxdt[:, ids] = self._diffequation(None, x[:, ids], u[:, (ids - 1)]) return (x, dxdt)
def _diffequation(self, t, x, u): '\twrite dynamics as first order ODE: dxdt = f(x(t))\n\t\t\tx is a 6x1 vector: [x, y, psi, vx, vy, omega]^T\n\t\t\tu is a 2x1 vector: [acc/pwm, steer]^T\n\t\t' steer = u[1] psi = x[2] vx = x[3] vy = x[4] omega = x[5] (Ffy, Frx, Fry) = self.calc_forces(x, u) dxdt = np.zeros(6) dxdt[0] = ((vx * np.cos(psi)) - (vy * np.sin(psi))) dxdt[1] = ((vx * np.sin(psi)) + (vy * np.cos(psi))) dxdt[2] = omega dxdt[3] = (((1 / self.mass) * (Frx - (Ffy * np.sin(steer)))) + (vy * omega)) dxdt[4] = (((1 / self.mass) * (Fry + (Ffy * np.cos(steer)))) - (vx * omega)) dxdt[5] = ((1 / self.Iz) * (((Ffy * self.lf) * np.cos(steer)) - (Fry * self.lr))) return dxdt
-43,071,347,659,589,810
write dynamics as first order ODE: dxdt = f(x(t)) x is a 6x1 vector: [x, y, psi, vx, vy, omega]^T u is a 2x1 vector: [acc/pwm, steer]^T
bayes_race/models/dynamic.py
_diffequation
KlrShaK/bayesrace
python
def _diffequation(self, t, x, u): '\twrite dynamics as first order ODE: dxdt = f(x(t))\n\t\t\tx is a 6x1 vector: [x, y, psi, vx, vy, omega]^T\n\t\t\tu is a 2x1 vector: [acc/pwm, steer]^T\n\t\t' steer = u[1] psi = x[2] vx = x[3] vy = x[4] omega = x[5] (Ffy, Frx, Fry) = self.calc_forces(x, u) dxdt = np.zeros(6) dxdt[0] = ((vx * np.cos(psi)) - (vy * np.sin(psi))) dxdt[1] = ((vx * np.sin(psi)) + (vy * np.cos(psi))) dxdt[2] = omega dxdt[3] = (((1 / self.mass) * (Frx - (Ffy * np.sin(steer)))) + (vy * omega)) dxdt[4] = (((1 / self.mass) * (Fry + (Ffy * np.cos(steer)))) - (vx * omega)) dxdt[5] = ((1 / self.Iz) * (((Ffy * self.lf) * np.cos(steer)) - (Fry * self.lr))) return dxdt
def casadi(self, x, u, dxdt): '\twrite dynamics as first order ODE: dxdt = f(x(t))\n\t\t\tx is a 6x1 vector: [x, y, psi, vx, vy, omega]^T\n\t\t\tu is a 2x1 vector: [acc/pwm, steer]^T\n\t\t\tdxdt is a casadi.SX variable\n\t\t' pwm = u[0] steer = u[1] psi = x[2] vx = x[3] vy = x[4] omega = x[5] vmin = 0.05 vy = cs.if_else((vx < vmin), 0, vy) omega = cs.if_else((vx < vmin), 0, omega) steer = cs.if_else((vx < vmin), 0, steer) vx = cs.if_else((vx < vmin), vmin, vx) Frx = ((((self.Cm1 - (self.Cm2 * vx)) * pwm) - self.Cr0) - (self.Cr2 * (vx ** 2))) alphaf = (steer - cs.atan2(((self.lf * omega) + vy), vx)) alphar = cs.atan2(((self.lr * omega) - vy), vx) Ffy = (self.Df * cs.sin((self.Cf * cs.arctan((self.Bf * alphaf))))) Fry = (self.Dr * cs.sin((self.Cr * cs.arctan((self.Br * alphar))))) dxdt[0] = ((vx * cs.cos(psi)) - (vy * cs.sin(psi))) dxdt[1] = ((vx * cs.sin(psi)) + (vy * cs.cos(psi))) dxdt[2] = omega dxdt[3] = (((1 / self.mass) * (Frx - (Ffy * cs.sin(steer)))) + (vy * omega)) dxdt[4] = (((1 / self.mass) * (Fry + (Ffy * cs.cos(steer)))) - (vx * omega)) dxdt[5] = ((1 / self.Iz) * (((Ffy * self.lf) * cs.cos(steer)) - (Fry * self.lr))) return dxdt
2,081,638,097,302,288,000
write dynamics as first order ODE: dxdt = f(x(t)) x is a 6x1 vector: [x, y, psi, vx, vy, omega]^T u is a 2x1 vector: [acc/pwm, steer]^T dxdt is a casadi.SX variable
bayes_race/models/dynamic.py
casadi
KlrShaK/bayesrace
python
def casadi(self, x, u, dxdt): '\twrite dynamics as first order ODE: dxdt = f(x(t))\n\t\t\tx is a 6x1 vector: [x, y, psi, vx, vy, omega]^T\n\t\t\tu is a 2x1 vector: [acc/pwm, steer]^T\n\t\t\tdxdt is a casadi.SX variable\n\t\t' pwm = u[0] steer = u[1] psi = x[2] vx = x[3] vy = x[4] omega = x[5] vmin = 0.05 vy = cs.if_else((vx < vmin), 0, vy) omega = cs.if_else((vx < vmin), 0, omega) steer = cs.if_else((vx < vmin), 0, steer) vx = cs.if_else((vx < vmin), vmin, vx) Frx = ((((self.Cm1 - (self.Cm2 * vx)) * pwm) - self.Cr0) - (self.Cr2 * (vx ** 2))) alphaf = (steer - cs.atan2(((self.lf * omega) + vy), vx)) alphar = cs.atan2(((self.lr * omega) - vy), vx) Ffy = (self.Df * cs.sin((self.Cf * cs.arctan((self.Bf * alphaf))))) Fry = (self.Dr * cs.sin((self.Cr * cs.arctan((self.Br * alphar))))) dxdt[0] = ((vx * cs.cos(psi)) - (vy * cs.sin(psi))) dxdt[1] = ((vx * cs.sin(psi)) + (vy * cs.cos(psi))) dxdt[2] = omega dxdt[3] = (((1 / self.mass) * (Frx - (Ffy * cs.sin(steer)))) + (vy * omega)) dxdt[4] = (((1 / self.mass) * (Fry + (Ffy * cs.cos(steer)))) - (vx * omega)) dxdt[5] = ((1 / self.Iz) * (((Ffy * self.lf) * cs.cos(steer)) - (Fry * self.lr))) return dxdt
def sim_discrete(self, x0, u, Ts): '\tsimulates a continuously linearized discrete model\n\t\t\tu is the input vector of size 2xn\n\t\t\tTs is the sampling time\n\t\t' n_steps = u.shape[1] x = np.zeros([6, (n_steps + 1)]) dxdt = np.zeros([6, (n_steps + 1)]) dxdt[:, 0] = self._diffequation(None, x0, [0, 0]) x[:, 0] = x0 for ids in range(1, (n_steps + 1)): g = self._diffequation(None, x[:, (ids - 1)], u[:, (ids - 1)]).reshape((- 1)) x[:, ids] = (x[:, (ids - 1)] + (g * Ts)) dxdt[:, ids] = self._diffequation(None, x[:, ids], u[:, (ids - 1)]) return (x, dxdt)
-4,940,027,757,049,915,000
simulates a continuously linearized discrete model u is the input vector of size 2xn Ts is the sampling time
bayes_race/models/dynamic.py
sim_discrete
KlrShaK/bayesrace
python
def sim_discrete(self, x0, u, Ts): '\tsimulates a continuously linearized discrete model\n\t\t\tu is the input vector of size 2xn\n\t\t\tTs is the sampling time\n\t\t' n_steps = u.shape[1] x = np.zeros([6, (n_steps + 1)]) dxdt = np.zeros([6, (n_steps + 1)]) dxdt[:, 0] = self._diffequation(None, x0, [0, 0]) x[:, 0] = x0 for ids in range(1, (n_steps + 1)): g = self._diffequation(None, x[:, (ids - 1)], u[:, (ids - 1)]).reshape((- 1)) x[:, ids] = (x[:, (ids - 1)] + (g * Ts)) dxdt[:, ids] = self._diffequation(None, x[:, ids], u[:, (ids - 1)]) return (x, dxdt)
def linearize(self, x0, u0): '\tlinearize at a given x0, u0\n\t\t\tfor a given continuous system dxdt = f(x(t))\n\t\t\tcalculate A = ∂f/∂x, B = ∂f/∂u, g = f evaluated at x0, u0\n\t\t\tA is 6x6, B is 6x2, g is 6x1\n\t\t' steer = u0[1] psi = x0[2] vx = x0[3] vy = x0[4] omega = x0[5] vmin = 0.05 if (vx < vmin): vy = 0 omega = 0 steer = 0 vx = vmin sindelta = np.sin(steer) cosdelta = np.cos(steer) sinpsi = np.sin(psi) cospsi = np.cos(psi) (Ffy, Frx, Fry, alphaf, alphar) = self.calc_forces(x0, u0, return_slip=True) if self.approx: dFfy_dvx = (((2 * self.Cf) * ((self.lf * omega) + vy)) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_dvy = ((((- 2) * self.Cf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_domega = (((((- 2) * self.Cf) * self.lf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFrx_dvx = 0 dFrx_dvu1 = 1 dFry_dvx = ((((- 2) * self.Cr) * ((self.lr * omega) - vy)) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_dvy = ((((- 2) * self.Cr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_domega = ((((2 * self.Cr) * self.lr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFfy_delta = (2 * self.Cf) else: dFfy_dalphaf = (((self.Bf * self.Cf) * self.Df) * np.cos((self.Cf * np.arctan((self.Bf * alphaf))))) dFfy_dalphaf *= (1 / (1 + ((self.Bf * alphaf) ** 2))) dFry_dalphar = (((self.Br * self.Cr) * self.Dr) * np.cos((self.Cr * np.arctan((self.Br * alphar))))) dFry_dalphar *= (1 / (1 + ((self.Br * alphar) ** 2))) dFfy_dvx = ((dFfy_dalphaf * ((self.lf * omega) + vy)) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_dvy = (((- dFfy_dalphaf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_domega = ((((- dFfy_dalphaf) * self.lf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) if self.input_acc: raise NotImplementedError pwm = u0[0] dFrx_dvx = (((- self.Cm2) * pwm) - ((2 * self.Cr2) * vx)) dFrx_dvu1 = (self.Cm1 - (self.Cm2 * vx)) dFry_dvx = (((- dFry_dalphar) * ((self.lr * omega) - vy)) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_dvy = (((- dFry_dalphar) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_domega = (((dFry_dalphar * self.lr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFfy_delta = dFfy_dalphaf f1_psi = (((- vx) * sinpsi) - (vy * cospsi)) f1_vx = cospsi f1_vy = (- sinpsi) f2_psi = ((vx * cospsi) - (vy * sinpsi)) f2_vx = sinpsi f2_vy = cospsi f4_vx = ((1 / self.mass) * (dFrx_dvx - (dFfy_dvx * sindelta))) f4_vy = ((1 / self.mass) * (((- dFfy_dvy) * sindelta) + (self.mass * omega))) f4_omega = ((1 / self.mass) * (((- dFfy_domega) * sindelta) + (self.mass * vy))) f5_vx = ((1 / self.mass) * ((dFry_dvx + (dFfy_dvx * cosdelta)) - (self.mass * omega))) f5_vy = ((1 / self.mass) * (dFry_dvy + (dFfy_dvy * cosdelta))) f5_omega = ((1 / self.mass) * ((dFry_domega + (dFfy_domega * cosdelta)) - (self.mass * vx))) f6_vx = ((1 / self.Iz) * (((dFfy_dvx * self.lf) * cosdelta) - (dFry_dvx * self.lr))) f6_vy = ((1 / self.Iz) * (((dFfy_dvy * self.lf) * cosdelta) - (dFry_dvy * self.lr))) f6_omega = ((1 / self.Iz) * (((dFfy_domega * self.lf) * cosdelta) - (dFry_domega * self.lr))) f4_u1 = dFrx_dvu1 f4_delta = ((1 / self.mass) * (((- dFfy_delta) * sindelta) - (Ffy * cosdelta))) f5_delta = ((1 / self.mass) * ((dFfy_delta * cosdelta) - (Ffy * sindelta))) f6_delta = ((1 / self.Iz) * (((dFfy_delta * self.lf) * cosdelta) - ((Ffy * self.lf) * sindelta))) A = np.array([[0, 0, f1_psi, f1_vx, f1_vy, 0], [0, 0, f2_psi, f2_vx, f2_vy, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, f4_vx, f4_vy, f4_omega], [0, 0, 0, f5_vx, f5_vy, f5_omega], [0, 0, 0, f6_vx, f6_vy, f6_omega]]) B = np.array([[0, 0], [0, 0], [0, 0], [f4_u1, f4_delta], [0, f5_delta], [0, f6_delta]]) g = self._diffequation(None, x0, u0).reshape((- 1)) return (A, B, g)
954,568,740,370,182,300
linearize at a given x0, u0 for a given continuous system dxdt = f(x(t)) calculate A = ∂f/∂x, B = ∂f/∂u, g = f evaluated at x0, u0 A is 6x6, B is 6x2, g is 6x1
bayes_race/models/dynamic.py
linearize
KlrShaK/bayesrace
python
def linearize(self, x0, u0): '\tlinearize at a given x0, u0\n\t\t\tfor a given continuous system dxdt = f(x(t))\n\t\t\tcalculate A = ∂f/∂x, B = ∂f/∂u, g = f evaluated at x0, u0\n\t\t\tA is 6x6, B is 6x2, g is 6x1\n\t\t' steer = u0[1] psi = x0[2] vx = x0[3] vy = x0[4] omega = x0[5] vmin = 0.05 if (vx < vmin): vy = 0 omega = 0 steer = 0 vx = vmin sindelta = np.sin(steer) cosdelta = np.cos(steer) sinpsi = np.sin(psi) cospsi = np.cos(psi) (Ffy, Frx, Fry, alphaf, alphar) = self.calc_forces(x0, u0, return_slip=True) if self.approx: dFfy_dvx = (((2 * self.Cf) * ((self.lf * omega) + vy)) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_dvy = ((((- 2) * self.Cf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_domega = (((((- 2) * self.Cf) * self.lf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFrx_dvx = 0 dFrx_dvu1 = 1 dFry_dvx = ((((- 2) * self.Cr) * ((self.lr * omega) - vy)) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_dvy = ((((- 2) * self.Cr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_domega = ((((2 * self.Cr) * self.lr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFfy_delta = (2 * self.Cf) else: dFfy_dalphaf = (((self.Bf * self.Cf) * self.Df) * np.cos((self.Cf * np.arctan((self.Bf * alphaf))))) dFfy_dalphaf *= (1 / (1 + ((self.Bf * alphaf) ** 2))) dFry_dalphar = (((self.Br * self.Cr) * self.Dr) * np.cos((self.Cr * np.arctan((self.Br * alphar))))) dFry_dalphar *= (1 / (1 + ((self.Br * alphar) ** 2))) dFfy_dvx = ((dFfy_dalphaf * ((self.lf * omega) + vy)) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_dvy = (((- dFfy_dalphaf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) dFfy_domega = ((((- dFfy_dalphaf) * self.lf) * vx) / ((((self.lf * omega) + vy) ** 2) + (vx ** 2))) if self.input_acc: raise NotImplementedError pwm = u0[0] dFrx_dvx = (((- self.Cm2) * pwm) - ((2 * self.Cr2) * vx)) dFrx_dvu1 = (self.Cm1 - (self.Cm2 * vx)) dFry_dvx = (((- dFry_dalphar) * ((self.lr * omega) - vy)) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_dvy = (((- dFry_dalphar) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFry_domega = (((dFry_dalphar * self.lr) * vx) / ((((self.lr * omega) - vy) ** 2) + (vx ** 2))) dFfy_delta = dFfy_dalphaf f1_psi = (((- vx) * sinpsi) - (vy * cospsi)) f1_vx = cospsi f1_vy = (- sinpsi) f2_psi = ((vx * cospsi) - (vy * sinpsi)) f2_vx = sinpsi f2_vy = cospsi f4_vx = ((1 / self.mass) * (dFrx_dvx - (dFfy_dvx * sindelta))) f4_vy = ((1 / self.mass) * (((- dFfy_dvy) * sindelta) + (self.mass * omega))) f4_omega = ((1 / self.mass) * (((- dFfy_domega) * sindelta) + (self.mass * vy))) f5_vx = ((1 / self.mass) * ((dFry_dvx + (dFfy_dvx * cosdelta)) - (self.mass * omega))) f5_vy = ((1 / self.mass) * (dFry_dvy + (dFfy_dvy * cosdelta))) f5_omega = ((1 / self.mass) * ((dFry_domega + (dFfy_domega * cosdelta)) - (self.mass * vx))) f6_vx = ((1 / self.Iz) * (((dFfy_dvx * self.lf) * cosdelta) - (dFry_dvx * self.lr))) f6_vy = ((1 / self.Iz) * (((dFfy_dvy * self.lf) * cosdelta) - (dFry_dvy * self.lr))) f6_omega = ((1 / self.Iz) * (((dFfy_domega * self.lf) * cosdelta) - (dFry_domega * self.lr))) f4_u1 = dFrx_dvu1 f4_delta = ((1 / self.mass) * (((- dFfy_delta) * sindelta) - (Ffy * cosdelta))) f5_delta = ((1 / self.mass) * ((dFfy_delta * cosdelta) - (Ffy * sindelta))) f6_delta = ((1 / self.Iz) * (((dFfy_delta * self.lf) * cosdelta) - ((Ffy * self.lf) * sindelta))) A = np.array([[0, 0, f1_psi, f1_vx, f1_vy, 0], [0, 0, f2_psi, f2_vx, f2_vy, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, f4_vx, f4_vy, f4_omega], [0, 0, 0, f5_vx, f5_vy, f5_omega], [0, 0, 0, f6_vx, f6_vy, f6_omega]]) B = np.array([[0, 0], [0, 0], [0, 0], [f4_u1, f4_delta], [0, f5_delta], [0, f6_delta]]) g = self._diffequation(None, x0, u0).reshape((- 1)) return (A, B, g)
def custom_callback(self, value): ' A custom callback for dealing with tool output.\n ' if ('%' in value): try: str_array = value.split(' ') label = value.replace(str_array[(len(str_array) - 1)], '').strip() progress = float(str_array[(len(str_array) - 1)].replace('%', '').strip()) self.progress_var.set(int(progress)) self.progress_label['text'] = label except ValueError as e: print('Problem converting parsed data into number: ', e) except Exception as e: print(e) else: self.print_line_to_output(value) self.update()
-7,362,732,948,371,499,000
A custom callback for dealing with tool output.
wb_runner.py
custom_callback
luzpaz/whitebox-tools
python
def custom_callback(self, value): ' \n ' if ('%' in value): try: str_array = value.split(' ') label = value.replace(str_array[(len(str_array) - 1)], ).strip() progress = float(str_array[(len(str_array) - 1)].replace('%', ).strip()) self.progress_var.set(int(progress)) self.progress_label['text'] = label except ValueError as e: print('Problem converting parsed data into number: ', e) except Exception as e: print(e) else: self.print_line_to_output(value) self.update()
def test_rollback(self, local_connection): 'test a basic rollback' users = self.tables.users connection = local_connection transaction = connection.begin() connection.execute(users.insert(), user_id=1, user_name='user1') connection.execute(users.insert(), user_id=2, user_name='user2') connection.execute(users.insert(), user_id=3, user_name='user3') transaction.rollback() result = connection.exec_driver_sql('select * from users') assert (len(result.fetchall()) == 0)
228,207,505,830,494,270
test a basic rollback
test/engine/test_transaction.py
test_rollback
418sec/sqlalchemy
python
def test_rollback(self, local_connection): users = self.tables.users connection = local_connection transaction = connection.begin() connection.execute(users.insert(), user_id=1, user_name='user1') connection.execute(users.insert(), user_id=2, user_name='user2') connection.execute(users.insert(), user_id=3, user_name='user3') transaction.rollback() result = connection.exec_driver_sql('select * from users') assert (len(result.fetchall()) == 0)
def test_rollback_deadlock(self): 'test that returning connections to the pool clears any object\n locks.' conn1 = testing.db.connect() conn2 = testing.db.connect() users = Table('deadlock_users', metadata, Column('user_id', INT, primary_key=True), Column('user_name', VARCHAR(20)), test_needs_acid=True) with conn1.begin(): users.create(conn1) conn1.exec_driver_sql('select * from deadlock_users') conn1.close() with conn2.begin(): users.drop(conn2) conn2.close()
-1,619,799,357,194,359,600
test that returning connections to the pool clears any object locks.
test/engine/test_transaction.py
test_rollback_deadlock
418sec/sqlalchemy
python
def test_rollback_deadlock(self): 'test that returning connections to the pool clears any object\n locks.' conn1 = testing.db.connect() conn2 = testing.db.connect() users = Table('deadlock_users', metadata, Column('user_id', INT, primary_key=True), Column('user_name', VARCHAR(20)), test_needs_acid=True) with conn1.begin(): users.create(conn1) conn1.exec_driver_sql('select * from deadlock_users') conn1.close() with conn2.begin(): users.drop(conn2) conn2.close()
def _nextPurge(self, source: BackupSource, backups, findNext=False): '\n Given a list of backups, decides if one should be purged.\n ' if ((not source.enabled()) or (len(backups) == 0)): return None if ((source.maxCount() == 0) and (not self.config.get(Setting.DELETE_AFTER_UPLOAD))): return None scheme = self._buildDeleteScheme(source, findNext=findNext) consider_purging = [] for backup in backups: source_backup = backup.getSource(source.name()) if ((source_backup is not None) and source_backup.considerForPurge() and (not backup.ignore())): consider_purging.append(backup) if (len(consider_purging) == 0): return None return scheme.getOldest(consider_purging)
-8,841,430,968,765,923,000
Given a list of backups, decides if one should be purged.
hassio-google-drive-backup/backup/model/model.py
_nextPurge
voxipbx/hassio-addons
python
def _nextPurge(self, source: BackupSource, backups, findNext=False): '\n \n ' if ((not source.enabled()) or (len(backups) == 0)): return None if ((source.maxCount() == 0) and (not self.config.get(Setting.DELETE_AFTER_UPLOAD))): return None scheme = self._buildDeleteScheme(source, findNext=findNext) consider_purging = [] for backup in backups: source_backup = backup.getSource(source.name()) if ((source_backup is not None) and source_backup.considerForPurge() and (not backup.ignore())): consider_purging.append(backup) if (len(consider_purging) == 0): return None return scheme.getOldest(consider_purging)
def __init__(self, position, discrete=False): '\n Initializes a observation in light dark domain.\n\n Args:\n position (tuple): position of the robot.\n ' self._discrete = discrete if (len(position) != 2): raise ValueError('Observation position must be a vector of length 2') if self._discrete: self.position = position else: self.position = (round(position[0], Observation.PRECISION), round(position[1], Observation.PRECISION))
-1,508,324,807,324,518,400
Initializes a observation in light dark domain. Args: position (tuple): position of the robot.
pomdp_problems/light_dark/domain/observation.py
__init__
Deathn0t/pomdp-py
python
def __init__(self, position, discrete=False): '\n Initializes a observation in light dark domain.\n\n Args:\n position (tuple): position of the robot.\n ' self._discrete = discrete if (len(position) != 2): raise ValueError('Observation position must be a vector of length 2') if self._discrete: self.position = position else: self.position = (round(position[0], Observation.PRECISION), round(position[1], Observation.PRECISION))
def make_marketing_pref_receiver(sender, instance, created, *args, **kwargs): '\n User model\n ' if created: MarketingPreference.objects.get_or_create(user=instance)
-4,182,596,920,956,513,000
User model
eCommerce-master/src/marketing/models.py
make_marketing_pref_receiver
felipebrigo/Python-Projects
python
def make_marketing_pref_receiver(sender, instance, created, *args, **kwargs): '\n \n ' if created: MarketingPreference.objects.get_or_create(user=instance)
def _find_x12(x12path=None, prefer_x13=True): '\n If x12path is not given, then either x13as[.exe] or x12a[.exe] must\n be found on the PATH. Otherwise, the environmental variable X12PATH or\n X13PATH must be defined. If prefer_x13 is True, only X13PATH is searched\n for. If it is false, only X12PATH is searched for.\n ' global _binary_names if ((x12path is not None) and x12path.endswith(_binary_names)): x12path = os.path.dirname(x12path) if (not prefer_x13): _binary_names = _binary_names[::(- 1)] if (x12path is None): x12path = os.getenv('X12PATH', '') if (not x12path): x12path = os.getenv('X13PATH', '') elif (x12path is None): x12path = os.getenv('X13PATH', '') if (not x12path): x12path = os.getenv('X12PATH', '') for binary in _binary_names: x12 = os.path.join(x12path, binary) try: subprocess.check_call(x12, stdout=subprocess.PIPE, stderr=subprocess.PIPE) return x12 except OSError: pass else: return False
5,155,090,809,194,233,000
If x12path is not given, then either x13as[.exe] or x12a[.exe] must be found on the PATH. Otherwise, the environmental variable X12PATH or X13PATH must be defined. If prefer_x13 is True, only X13PATH is searched for. If it is false, only X12PATH is searched for.
statsmodels/tsa/x13.py
_find_x12
diego-mazon/statsmodels
python
def _find_x12(x12path=None, prefer_x13=True): '\n If x12path is not given, then either x13as[.exe] or x12a[.exe] must\n be found on the PATH. Otherwise, the environmental variable X12PATH or\n X13PATH must be defined. If prefer_x13 is True, only X13PATH is searched\n for. If it is false, only X12PATH is searched for.\n ' global _binary_names if ((x12path is not None) and x12path.endswith(_binary_names)): x12path = os.path.dirname(x12path) if (not prefer_x13): _binary_names = _binary_names[::(- 1)] if (x12path is None): x12path = os.getenv('X12PATH', ) if (not x12path): x12path = os.getenv('X13PATH', ) elif (x12path is None): x12path = os.getenv('X13PATH', ) if (not x12path): x12path = os.getenv('X12PATH', ) for binary in _binary_names: x12 = os.path.join(x12path, binary) try: subprocess.check_call(x12, stdout=subprocess.PIPE, stderr=subprocess.PIPE) return x12 except OSError: pass else: return False
def _clean_order(order): '\n Takes something like (1 1 0)(0 1 1) and returns a arma order, sarma\n order tuple. Also accepts (1 1 0) and return arma order and (0, 0, 0)\n ' order = re.findall('\\([0-9 ]*?\\)', order) def clean(x): return tuple(map(int, re.sub('[()]', '', x).split(' '))) if (len(order) > 1): (order, sorder) = map(clean, order) else: order = clean(order[0]) sorder = (0, 0, 0) return (order, sorder)
-271,275,661,211,769,340
Takes something like (1 1 0)(0 1 1) and returns a arma order, sarma order tuple. Also accepts (1 1 0) and return arma order and (0, 0, 0)
statsmodels/tsa/x13.py
_clean_order
diego-mazon/statsmodels
python
def _clean_order(order): '\n Takes something like (1 1 0)(0 1 1) and returns a arma order, sarma\n order tuple. Also accepts (1 1 0) and return arma order and (0, 0, 0)\n ' order = re.findall('\\([0-9 ]*?\\)', order) def clean(x): return tuple(map(int, re.sub('[()]', , x).split(' '))) if (len(order) > 1): (order, sorder) = map(clean, order) else: order = clean(order[0]) sorder = (0, 0, 0) return (order, sorder)
def _convert_out_to_series(x, dates, name): '\n Convert x to a DataFrame where x is a string in the format given by\n x-13arima-seats output.\n ' from io import StringIO from pandas import read_csv out = read_csv(StringIO(x), skiprows=2, header=None, sep='\t', engine='python') return out.set_index(dates).rename(columns={1: name})[name]
8,948,449,459,947,929,000
Convert x to a DataFrame where x is a string in the format given by x-13arima-seats output.
statsmodels/tsa/x13.py
_convert_out_to_series
diego-mazon/statsmodels
python
def _convert_out_to_series(x, dates, name): '\n Convert x to a DataFrame where x is a string in the format given by\n x-13arima-seats output.\n ' from io import StringIO from pandas import read_csv out = read_csv(StringIO(x), skiprows=2, header=None, sep='\t', engine='python') return out.set_index(dates).rename(columns={1: name})[name]
def x13_arima_analysis(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, retspec=False, speconly=False, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True): '\n Perform x13-arima analysis for monthly or quarterly data.\n\n Parameters\n ----------\n endog : array_like, pandas.Series\n The series to model. It is best to use a pandas object with a\n DatetimeIndex or PeriodIndex. However, you can pass an array-like\n object. If your object does not have a dates index then ``start`` and\n ``freq`` are not optional.\n maxorder : tuple\n The maximum order of the regular and seasonal ARMA polynomials to\n examine during the model identification. The order for the regular\n polynomial must be greater than zero and no larger than 4. The\n order for the seasonal polynomial may be 1 or 2.\n maxdiff : tuple\n The maximum orders for regular and seasonal differencing in the\n automatic differencing procedure. Acceptable inputs for regular\n differencing are 1 and 2. The maximum order for seasonal differencing\n is 1. If ``diff`` is specified then ``maxdiff`` should be None.\n Otherwise, ``diff`` will be ignored. See also ``diff``.\n diff : tuple\n Fixes the orders of differencing for the regular and seasonal\n differencing. Regular differencing may be 0, 1, or 2. Seasonal\n differencing may be 0 or 1. ``maxdiff`` must be None, otherwise\n ``diff`` is ignored.\n exog : array_like\n Exogenous variables.\n log : bool or None\n If None, it is automatically determined whether to log the series or\n not. If False, logs are not taken. If True, logs are taken.\n outlier : bool\n Whether or not outliers are tested for and corrected, if detected.\n trading : bool\n Whether or not trading day effects are tested for.\n forecast_years : int\n Number of forecasts produced. The default is one year.\n retspec : bool\n Whether to return the created specification file. Can be useful for\n debugging.\n speconly : bool\n Whether to create the specification file and then return it without\n performing the analysis. Can be useful for debugging.\n start : str, datetime\n Must be given if ``endog`` does not have date information in its index.\n Anything accepted by pandas.DatetimeIndex for the start value.\n freq : str\n Must be givein if ``endog`` does not have date information in its\n index. Anything accepted by pandas.DatetimeIndex for the freq value.\n print_stdout : bool\n The stdout from X12/X13 is suppressed. To print it out, set this\n to True. Default is False.\n x12path : str or None\n The path to x12 or x13 binary. If None, the program will attempt\n to find x13as or x12a on the PATH or by looking at X13PATH or\n X12PATH depending on the value of prefer_x13.\n prefer_x13 : bool\n If True, will look for x13as first and will fallback to the X13PATH\n environmental variable. If False, will look for x12a first and will\n fallback to the X12PATH environmental variable. If x12path points\n to the path for the X12/X13 binary, it does nothing.\n\n\n Returns\n -------\n res : Bunch\n A bunch object with the following attributes:\n\n - results : str\n The full output from the X12/X13 run.\n - seasadj : pandas.Series\n The final seasonally adjusted ``endog``\n - trend : pandas.Series\n The trend-cycle component of ``endog``\n - irregular : pandas.Series\n The final irregular component of ``endog``\n - stdout : str\n The captured stdout produced by x12/x13.\n - spec : str, optional\n Returned if ``retspec`` is True. The only thing returned if\n ``speconly`` is True.\n\n Notes\n -----\n This works by creating a specification file, writing it to a temporary\n directory, invoking X12/X13 in a subprocess, and reading the output\n directory, invoking exog12/X13 in a subprocess, and reading the output\n back in.\n ' x12path = _check_x12(x12path) if (not isinstance(endog, (pd.DataFrame, pd.Series))): if ((start is None) or (freq is None)): raise ValueError('start and freq cannot be none if endog is not a pandas object') endog = pd.Series(endog, index=pd.DatetimeIndex(start=start, periods=len(endog), freq=freq)) spec_obj = pandas_to_series_spec(endog) spec = spec_obj.create_spec() spec += 'transform{{function={0}}}\n'.format(_log_to_x12[log]) if outlier: spec += 'outlier{}\n' options = _make_automdl_options(maxorder, maxdiff, diff) spec += 'automdl{{{0}}}\n'.format(options) spec += _make_regression_options(trading, exog) spec += _make_forecast_options(forecast_years) spec += 'x11{ save=(d11 d12 d13) }' if speconly: return spec ftempin = tempfile.NamedTemporaryFile(delete=False, suffix='.spc') ftempout = tempfile.NamedTemporaryFile(delete=False) try: ftempin.write(spec.encode('utf8')) ftempin.close() ftempout.close() p = run_spec(x12path, ftempin.name[:(- 4)], ftempout.name) p.wait() stdout = p.stdout.read() if print_stdout: print(p.stdout.read()) errors = _open_and_read((ftempout.name + '.err')) _check_errors(errors) results = _open_and_read((ftempout.name + '.out')) seasadj = _open_and_read((ftempout.name + '.d11')) trend = _open_and_read((ftempout.name + '.d12')) irregular = _open_and_read((ftempout.name + '.d13')) finally: try: os.remove(ftempin.name) os.remove(ftempout.name) except OSError: if os.path.exists(ftempin.name): warn('Failed to delete resource {0}'.format(ftempin.name), IOWarning) if os.path.exists(ftempout.name): warn('Failed to delete resource {0}'.format(ftempout.name), IOWarning) seasadj = _convert_out_to_series(seasadj, endog.index, 'seasadj') trend = _convert_out_to_series(trend, endog.index, 'trend') irregular = _convert_out_to_series(irregular, endog.index, 'irregular') if (not retspec): res = X13ArimaAnalysisResult(observed=endog, results=results, seasadj=seasadj, trend=trend, irregular=irregular, stdout=stdout) else: res = X13ArimaAnalysisResult(observed=endog, results=results, seasadj=seasadj, trend=trend, irregular=irregular, stdout=stdout, spec=spec) return res
6,818,303,355,341,706,000
Perform x13-arima analysis for monthly or quarterly data. Parameters ---------- endog : array_like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex or PeriodIndex. However, you can pass an array-like object. If your object does not have a dates index then ``start`` and ``freq`` are not optional. maxorder : tuple The maximum order of the regular and seasonal ARMA polynomials to examine during the model identification. The order for the regular polynomial must be greater than zero and no larger than 4. The order for the seasonal polynomial may be 1 or 2. maxdiff : tuple The maximum orders for regular and seasonal differencing in the automatic differencing procedure. Acceptable inputs for regular differencing are 1 and 2. The maximum order for seasonal differencing is 1. If ``diff`` is specified then ``maxdiff`` should be None. Otherwise, ``diff`` will be ignored. See also ``diff``. diff : tuple Fixes the orders of differencing for the regular and seasonal differencing. Regular differencing may be 0, 1, or 2. Seasonal differencing may be 0 or 1. ``maxdiff`` must be None, otherwise ``diff`` is ignored. exog : array_like Exogenous variables. log : bool or None If None, it is automatically determined whether to log the series or not. If False, logs are not taken. If True, logs are taken. outlier : bool Whether or not outliers are tested for and corrected, if detected. trading : bool Whether or not trading day effects are tested for. forecast_years : int Number of forecasts produced. The default is one year. retspec : bool Whether to return the created specification file. Can be useful for debugging. speconly : bool Whether to create the specification file and then return it without performing the analysis. Can be useful for debugging. start : str, datetime Must be given if ``endog`` does not have date information in its index. Anything accepted by pandas.DatetimeIndex for the start value. freq : str Must be givein if ``endog`` does not have date information in its index. Anything accepted by pandas.DatetimeIndex for the freq value. print_stdout : bool The stdout from X12/X13 is suppressed. To print it out, set this to True. Default is False. x12path : str or None The path to x12 or x13 binary. If None, the program will attempt to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH depending on the value of prefer_x13. prefer_x13 : bool If True, will look for x13as first and will fallback to the X13PATH environmental variable. If False, will look for x12a first and will fallback to the X12PATH environmental variable. If x12path points to the path for the X12/X13 binary, it does nothing. Returns ------- res : Bunch A bunch object with the following attributes: - results : str The full output from the X12/X13 run. - seasadj : pandas.Series The final seasonally adjusted ``endog`` - trend : pandas.Series The trend-cycle component of ``endog`` - irregular : pandas.Series The final irregular component of ``endog`` - stdout : str The captured stdout produced by x12/x13. - spec : str, optional Returned if ``retspec`` is True. The only thing returned if ``speconly`` is True. Notes ----- This works by creating a specification file, writing it to a temporary directory, invoking X12/X13 in a subprocess, and reading the output directory, invoking exog12/X13 in a subprocess, and reading the output back in.
statsmodels/tsa/x13.py
x13_arima_analysis
diego-mazon/statsmodels
python
def x13_arima_analysis(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, retspec=False, speconly=False, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True): '\n Perform x13-arima analysis for monthly or quarterly data.\n\n Parameters\n ----------\n endog : array_like, pandas.Series\n The series to model. It is best to use a pandas object with a\n DatetimeIndex or PeriodIndex. However, you can pass an array-like\n object. If your object does not have a dates index then ``start`` and\n ``freq`` are not optional.\n maxorder : tuple\n The maximum order of the regular and seasonal ARMA polynomials to\n examine during the model identification. The order for the regular\n polynomial must be greater than zero and no larger than 4. The\n order for the seasonal polynomial may be 1 or 2.\n maxdiff : tuple\n The maximum orders for regular and seasonal differencing in the\n automatic differencing procedure. Acceptable inputs for regular\n differencing are 1 and 2. The maximum order for seasonal differencing\n is 1. If ``diff`` is specified then ``maxdiff`` should be None.\n Otherwise, ``diff`` will be ignored. See also ``diff``.\n diff : tuple\n Fixes the orders of differencing for the regular and seasonal\n differencing. Regular differencing may be 0, 1, or 2. Seasonal\n differencing may be 0 or 1. ``maxdiff`` must be None, otherwise\n ``diff`` is ignored.\n exog : array_like\n Exogenous variables.\n log : bool or None\n If None, it is automatically determined whether to log the series or\n not. If False, logs are not taken. If True, logs are taken.\n outlier : bool\n Whether or not outliers are tested for and corrected, if detected.\n trading : bool\n Whether or not trading day effects are tested for.\n forecast_years : int\n Number of forecasts produced. The default is one year.\n retspec : bool\n Whether to return the created specification file. Can be useful for\n debugging.\n speconly : bool\n Whether to create the specification file and then return it without\n performing the analysis. Can be useful for debugging.\n start : str, datetime\n Must be given if ``endog`` does not have date information in its index.\n Anything accepted by pandas.DatetimeIndex for the start value.\n freq : str\n Must be givein if ``endog`` does not have date information in its\n index. Anything accepted by pandas.DatetimeIndex for the freq value.\n print_stdout : bool\n The stdout from X12/X13 is suppressed. To print it out, set this\n to True. Default is False.\n x12path : str or None\n The path to x12 or x13 binary. If None, the program will attempt\n to find x13as or x12a on the PATH or by looking at X13PATH or\n X12PATH depending on the value of prefer_x13.\n prefer_x13 : bool\n If True, will look for x13as first and will fallback to the X13PATH\n environmental variable. If False, will look for x12a first and will\n fallback to the X12PATH environmental variable. If x12path points\n to the path for the X12/X13 binary, it does nothing.\n\n\n Returns\n -------\n res : Bunch\n A bunch object with the following attributes:\n\n - results : str\n The full output from the X12/X13 run.\n - seasadj : pandas.Series\n The final seasonally adjusted ``endog``\n - trend : pandas.Series\n The trend-cycle component of ``endog``\n - irregular : pandas.Series\n The final irregular component of ``endog``\n - stdout : str\n The captured stdout produced by x12/x13.\n - spec : str, optional\n Returned if ``retspec`` is True. The only thing returned if\n ``speconly`` is True.\n\n Notes\n -----\n This works by creating a specification file, writing it to a temporary\n directory, invoking X12/X13 in a subprocess, and reading the output\n directory, invoking exog12/X13 in a subprocess, and reading the output\n back in.\n ' x12path = _check_x12(x12path) if (not isinstance(endog, (pd.DataFrame, pd.Series))): if ((start is None) or (freq is None)): raise ValueError('start and freq cannot be none if endog is not a pandas object') endog = pd.Series(endog, index=pd.DatetimeIndex(start=start, periods=len(endog), freq=freq)) spec_obj = pandas_to_series_spec(endog) spec = spec_obj.create_spec() spec += 'transform{{function={0}}}\n'.format(_log_to_x12[log]) if outlier: spec += 'outlier{}\n' options = _make_automdl_options(maxorder, maxdiff, diff) spec += 'automdl{{{0}}}\n'.format(options) spec += _make_regression_options(trading, exog) spec += _make_forecast_options(forecast_years) spec += 'x11{ save=(d11 d12 d13) }' if speconly: return spec ftempin = tempfile.NamedTemporaryFile(delete=False, suffix='.spc') ftempout = tempfile.NamedTemporaryFile(delete=False) try: ftempin.write(spec.encode('utf8')) ftempin.close() ftempout.close() p = run_spec(x12path, ftempin.name[:(- 4)], ftempout.name) p.wait() stdout = p.stdout.read() if print_stdout: print(p.stdout.read()) errors = _open_and_read((ftempout.name + '.err')) _check_errors(errors) results = _open_and_read((ftempout.name + '.out')) seasadj = _open_and_read((ftempout.name + '.d11')) trend = _open_and_read((ftempout.name + '.d12')) irregular = _open_and_read((ftempout.name + '.d13')) finally: try: os.remove(ftempin.name) os.remove(ftempout.name) except OSError: if os.path.exists(ftempin.name): warn('Failed to delete resource {0}'.format(ftempin.name), IOWarning) if os.path.exists(ftempout.name): warn('Failed to delete resource {0}'.format(ftempout.name), IOWarning) seasadj = _convert_out_to_series(seasadj, endog.index, 'seasadj') trend = _convert_out_to_series(trend, endog.index, 'trend') irregular = _convert_out_to_series(irregular, endog.index, 'irregular') if (not retspec): res = X13ArimaAnalysisResult(observed=endog, results=results, seasadj=seasadj, trend=trend, irregular=irregular, stdout=stdout) else: res = X13ArimaAnalysisResult(observed=endog, results=results, seasadj=seasadj, trend=trend, irregular=irregular, stdout=stdout, spec=spec) return res
def x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True): '\n Perform automatic seasonal ARIMA order identification using x12/x13 ARIMA.\n\n Parameters\n ----------\n endog : array_like, pandas.Series\n The series to model. It is best to use a pandas object with a\n DatetimeIndex or PeriodIndex. However, you can pass an array-like\n object. If your object does not have a dates index then ``start`` and\n ``freq`` are not optional.\n maxorder : tuple\n The maximum order of the regular and seasonal ARMA polynomials to\n examine during the model identification. The order for the regular\n polynomial must be greater than zero and no larger than 4. The\n order for the seasonal polynomial may be 1 or 2.\n maxdiff : tuple\n The maximum orders for regular and seasonal differencing in the\n automatic differencing procedure. Acceptable inputs for regular\n differencing are 1 and 2. The maximum order for seasonal differencing\n is 1. If ``diff`` is specified then ``maxdiff`` should be None.\n Otherwise, ``diff`` will be ignored. See also ``diff``.\n diff : tuple\n Fixes the orders of differencing for the regular and seasonal\n differencing. Regular differencing may be 0, 1, or 2. Seasonal\n differencing may be 0 or 1. ``maxdiff`` must be None, otherwise\n ``diff`` is ignored.\n exog : array_like\n Exogenous variables.\n log : bool or None\n If None, it is automatically determined whether to log the series or\n not. If False, logs are not taken. If True, logs are taken.\n outlier : bool\n Whether or not outliers are tested for and corrected, if detected.\n trading : bool\n Whether or not trading day effects are tested for.\n forecast_years : int\n Number of forecasts produced. The default is one year.\n start : str, datetime\n Must be given if ``endog`` does not have date information in its index.\n Anything accepted by pandas.DatetimeIndex for the start value.\n freq : str\n Must be givein if ``endog`` does not have date information in its\n index. Anything accepted by pandas.DatetimeIndex for the freq value.\n print_stdout : bool\n The stdout from X12/X13 is suppressed. To print it out, set this\n to True. Default is False.\n x12path : str or None\n The path to x12 or x13 binary. If None, the program will attempt\n to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH\n depending on the value of prefer_x13.\n prefer_x13 : bool\n If True, will look for x13as first and will fallback to the X13PATH\n environmental variable. If False, will look for x12a first and will\n fallback to the X12PATH environmental variable. If x12path points\n to the path for the X12/X13 binary, it does nothing.\n\n Returns\n -------\n results : Bunch\n A bunch object that has the following attributes:\n\n - order : tuple\n The regular order\n - sorder : tuple\n The seasonal order\n - include_mean : bool\n Whether to include a mean or not\n - results : str\n The full results from the X12/X13 analysis\n - stdout : str\n The captured stdout from the X12/X13 analysis\n\n Notes\n -----\n This works by creating a specification file, writing it to a temporary\n directory, invoking X12/X13 in a subprocess, and reading the output back\n in.\n ' results = x13_arima_analysis(endog, x12path=x12path, exog=exog, log=log, outlier=outlier, trading=trading, forecast_years=forecast_years, maxorder=maxorder, maxdiff=maxdiff, diff=diff, start=start, freq=freq, prefer_x13=prefer_x13) model = re.search('(?<=Final automatic model choice : ).*', results.results) order = model.group() if re.search('Mean is not significant', results.results): include_mean = False elif re.search('Constant', results.results): include_mean = True else: include_mean = False (order, sorder) = _clean_order(order) res = Bunch(order=order, sorder=sorder, include_mean=include_mean, results=results.results, stdout=results.stdout) return res
-2,889,664,093,120,679,000
Perform automatic seasonal ARIMA order identification using x12/x13 ARIMA. Parameters ---------- endog : array_like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex or PeriodIndex. However, you can pass an array-like object. If your object does not have a dates index then ``start`` and ``freq`` are not optional. maxorder : tuple The maximum order of the regular and seasonal ARMA polynomials to examine during the model identification. The order for the regular polynomial must be greater than zero and no larger than 4. The order for the seasonal polynomial may be 1 or 2. maxdiff : tuple The maximum orders for regular and seasonal differencing in the automatic differencing procedure. Acceptable inputs for regular differencing are 1 and 2. The maximum order for seasonal differencing is 1. If ``diff`` is specified then ``maxdiff`` should be None. Otherwise, ``diff`` will be ignored. See also ``diff``. diff : tuple Fixes the orders of differencing for the regular and seasonal differencing. Regular differencing may be 0, 1, or 2. Seasonal differencing may be 0 or 1. ``maxdiff`` must be None, otherwise ``diff`` is ignored. exog : array_like Exogenous variables. log : bool or None If None, it is automatically determined whether to log the series or not. If False, logs are not taken. If True, logs are taken. outlier : bool Whether or not outliers are tested for and corrected, if detected. trading : bool Whether or not trading day effects are tested for. forecast_years : int Number of forecasts produced. The default is one year. start : str, datetime Must be given if ``endog`` does not have date information in its index. Anything accepted by pandas.DatetimeIndex for the start value. freq : str Must be givein if ``endog`` does not have date information in its index. Anything accepted by pandas.DatetimeIndex for the freq value. print_stdout : bool The stdout from X12/X13 is suppressed. To print it out, set this to True. Default is False. x12path : str or None The path to x12 or x13 binary. If None, the program will attempt to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH depending on the value of prefer_x13. prefer_x13 : bool If True, will look for x13as first and will fallback to the X13PATH environmental variable. If False, will look for x12a first and will fallback to the X12PATH environmental variable. If x12path points to the path for the X12/X13 binary, it does nothing. Returns ------- results : Bunch A bunch object that has the following attributes: - order : tuple The regular order - sorder : tuple The seasonal order - include_mean : bool Whether to include a mean or not - results : str The full results from the X12/X13 analysis - stdout : str The captured stdout from the X12/X13 analysis Notes ----- This works by creating a specification file, writing it to a temporary directory, invoking X12/X13 in a subprocess, and reading the output back in.
statsmodels/tsa/x13.py
x13_arima_select_order
diego-mazon/statsmodels
python
def x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True): '\n Perform automatic seasonal ARIMA order identification using x12/x13 ARIMA.\n\n Parameters\n ----------\n endog : array_like, pandas.Series\n The series to model. It is best to use a pandas object with a\n DatetimeIndex or PeriodIndex. However, you can pass an array-like\n object. If your object does not have a dates index then ``start`` and\n ``freq`` are not optional.\n maxorder : tuple\n The maximum order of the regular and seasonal ARMA polynomials to\n examine during the model identification. The order for the regular\n polynomial must be greater than zero and no larger than 4. The\n order for the seasonal polynomial may be 1 or 2.\n maxdiff : tuple\n The maximum orders for regular and seasonal differencing in the\n automatic differencing procedure. Acceptable inputs for regular\n differencing are 1 and 2. The maximum order for seasonal differencing\n is 1. If ``diff`` is specified then ``maxdiff`` should be None.\n Otherwise, ``diff`` will be ignored. See also ``diff``.\n diff : tuple\n Fixes the orders of differencing for the regular and seasonal\n differencing. Regular differencing may be 0, 1, or 2. Seasonal\n differencing may be 0 or 1. ``maxdiff`` must be None, otherwise\n ``diff`` is ignored.\n exog : array_like\n Exogenous variables.\n log : bool or None\n If None, it is automatically determined whether to log the series or\n not. If False, logs are not taken. If True, logs are taken.\n outlier : bool\n Whether or not outliers are tested for and corrected, if detected.\n trading : bool\n Whether or not trading day effects are tested for.\n forecast_years : int\n Number of forecasts produced. The default is one year.\n start : str, datetime\n Must be given if ``endog`` does not have date information in its index.\n Anything accepted by pandas.DatetimeIndex for the start value.\n freq : str\n Must be givein if ``endog`` does not have date information in its\n index. Anything accepted by pandas.DatetimeIndex for the freq value.\n print_stdout : bool\n The stdout from X12/X13 is suppressed. To print it out, set this\n to True. Default is False.\n x12path : str or None\n The path to x12 or x13 binary. If None, the program will attempt\n to find x13as or x12a on the PATH or by looking at X13PATH or X12PATH\n depending on the value of prefer_x13.\n prefer_x13 : bool\n If True, will look for x13as first and will fallback to the X13PATH\n environmental variable. If False, will look for x12a first and will\n fallback to the X12PATH environmental variable. If x12path points\n to the path for the X12/X13 binary, it does nothing.\n\n Returns\n -------\n results : Bunch\n A bunch object that has the following attributes:\n\n - order : tuple\n The regular order\n - sorder : tuple\n The seasonal order\n - include_mean : bool\n Whether to include a mean or not\n - results : str\n The full results from the X12/X13 analysis\n - stdout : str\n The captured stdout from the X12/X13 analysis\n\n Notes\n -----\n This works by creating a specification file, writing it to a temporary\n directory, invoking X12/X13 in a subprocess, and reading the output back\n in.\n ' results = x13_arima_analysis(endog, x12path=x12path, exog=exog, log=log, outlier=outlier, trading=trading, forecast_years=forecast_years, maxorder=maxorder, maxdiff=maxdiff, diff=diff, start=start, freq=freq, prefer_x13=prefer_x13) model = re.search('(?<=Final automatic model choice : ).*', results.results) order = model.group() if re.search('Mean is not significant', results.results): include_mean = False elif re.search('Constant', results.results): include_mean = True else: include_mean = False (order, sorder) = _clean_order(order) res = Bunch(order=order, sorder=sorder, include_mean=include_mean, results=results.results, stdout=results.stdout) return res
def send_email(to: AddressesType, subject: str, html_content: str, files: Optional[AddressesType]=None, cc: Optional[AddressesType]=None, bcc: Optional[AddressesType]=None, sandbox_mode: bool=False, **kwargs) -> None: '\n Send an email with html content using `Sendgrid <https://sendgrid.com/>`__.\n\n .. note::\n For more information, see :ref:`email-configuration-sendgrid`\n ' if (files is None): files = [] mail = Mail() from_email = (kwargs.get('from_email') or os.environ.get('SENDGRID_MAIL_FROM')) from_name = (kwargs.get('from_name') or os.environ.get('SENDGRID_MAIL_SENDER')) mail.from_email = Email(from_email, from_name) mail.subject = subject mail.mail_settings = MailSettings() if sandbox_mode: mail.mail_settings.sandbox_mode = SandBoxMode(enable=True) personalization = Personalization() to = get_email_address_list(to) for to_address in to: personalization.add_to(Email(to_address)) if cc: cc = get_email_address_list(cc) for cc_address in cc: personalization.add_cc(Email(cc_address)) if bcc: bcc = get_email_address_list(bcc) for bcc_address in bcc: personalization.add_bcc(Email(bcc_address)) pers_custom_args = kwargs.get('personalization_custom_args', None) if isinstance(pers_custom_args, dict): for key in pers_custom_args.keys(): personalization.add_custom_arg(CustomArg(key, pers_custom_args[key])) mail.add_personalization(personalization) mail.add_content(Content('text/html', html_content)) categories = kwargs.get('categories', []) for cat in categories: mail.add_category(Category(cat)) for fname in files: basename = os.path.basename(fname) with open(fname, 'rb') as file: content = base64.b64encode(file.read()).decode('utf-8') attachment = Attachment(file_content=content, file_type=mimetypes.guess_type(basename)[0], file_name=basename, disposition='attachment', content_id=f'<{basename}>') mail.add_attachment(attachment) _post_sendgrid_mail(mail.get())
-1,098,207,822,008,200,100
Send an email with html content using `Sendgrid <https://sendgrid.com/>`__. .. note:: For more information, see :ref:`email-configuration-sendgrid`
airflow/providers/sendgrid/utils/emailer.py
send_email
AI-ML-Projects/airflow
python
def send_email(to: AddressesType, subject: str, html_content: str, files: Optional[AddressesType]=None, cc: Optional[AddressesType]=None, bcc: Optional[AddressesType]=None, sandbox_mode: bool=False, **kwargs) -> None: '\n Send an email with html content using `Sendgrid <https://sendgrid.com/>`__.\n\n .. note::\n For more information, see :ref:`email-configuration-sendgrid`\n ' if (files is None): files = [] mail = Mail() from_email = (kwargs.get('from_email') or os.environ.get('SENDGRID_MAIL_FROM')) from_name = (kwargs.get('from_name') or os.environ.get('SENDGRID_MAIL_SENDER')) mail.from_email = Email(from_email, from_name) mail.subject = subject mail.mail_settings = MailSettings() if sandbox_mode: mail.mail_settings.sandbox_mode = SandBoxMode(enable=True) personalization = Personalization() to = get_email_address_list(to) for to_address in to: personalization.add_to(Email(to_address)) if cc: cc = get_email_address_list(cc) for cc_address in cc: personalization.add_cc(Email(cc_address)) if bcc: bcc = get_email_address_list(bcc) for bcc_address in bcc: personalization.add_bcc(Email(bcc_address)) pers_custom_args = kwargs.get('personalization_custom_args', None) if isinstance(pers_custom_args, dict): for key in pers_custom_args.keys(): personalization.add_custom_arg(CustomArg(key, pers_custom_args[key])) mail.add_personalization(personalization) mail.add_content(Content('text/html', html_content)) categories = kwargs.get('categories', []) for cat in categories: mail.add_category(Category(cat)) for fname in files: basename = os.path.basename(fname) with open(fname, 'rb') as file: content = base64.b64encode(file.read()).decode('utf-8') attachment = Attachment(file_content=content, file_type=mimetypes.guess_type(basename)[0], file_name=basename, disposition='attachment', content_id=f'<{basename}>') mail.add_attachment(attachment) _post_sendgrid_mail(mail.get())
def corpus_reader(path): 'Lê as extensões dos arquivos .xml no caminho especificado como path e\n retorna uma tupla com duas listas.Uma lista contém os paths para os arquivos\n .xml e a outra contém os arquivos Document gerados para aquele arquilo .xml\n ' prog = re.compile('(\\.xml)$') doc_list = [] f = [] fps = [] for (dirpath, dirnames, filenames) in os.walk(path): for filename in filenames: fps.append(os.path.normpath(os.path.join(dirpath, filename))) for path in fps: if re.search(prog, path): f.append(path) doc_list.append(Document(path)) return (f, doc_list)
7,513,357,988,800,923,000
Lê as extensões dos arquivos .xml no caminho especificado como path e retorna uma tupla com duas listas.Uma lista contém os paths para os arquivos .xml e a outra contém os arquivos Document gerados para aquele arquilo .xml
complexidade_textual.py
corpus_reader
lflage/complexidade_textual
python
def corpus_reader(path): 'Lê as extensões dos arquivos .xml no caminho especificado como path e\n retorna uma tupla com duas listas.Uma lista contém os paths para os arquivos\n .xml e a outra contém os arquivos Document gerados para aquele arquilo .xml\n ' prog = re.compile('(\\.xml)$') doc_list = [] f = [] fps = [] for (dirpath, dirnames, filenames) in os.walk(path): for filename in filenames: fps.append(os.path.normpath(os.path.join(dirpath, filename))) for path in fps: if re.search(prog, path): f.append(path) doc_list.append(Document(path)) return (f, doc_list)
def corpus_yeeter(path): 'Similar ao corpus_reader. Recebe um caminho para a pasta contendo o\n corpus e cria um generator. Cada iteração retorna uma tupla contendo um\n caminho para o arquivo .xml e o objeto Document criado a partir do mesmo\n ' prog = re.compile('(\\.xml)$') for (dirpath, dirnames, filenames) in os.walk(path): for filename in filenames: if re.search(prog, filename): path = os.path.normpath(os.path.join(dirpath, filename)) (yield (path, Document(path)))
5,500,897,651,855,036,000
Similar ao corpus_reader. Recebe um caminho para a pasta contendo o corpus e cria um generator. Cada iteração retorna uma tupla contendo um caminho para o arquivo .xml e o objeto Document criado a partir do mesmo
complexidade_textual.py
corpus_yeeter
lflage/complexidade_textual
python
def corpus_yeeter(path): 'Similar ao corpus_reader. Recebe um caminho para a pasta contendo o\n corpus e cria um generator. Cada iteração retorna uma tupla contendo um\n caminho para o arquivo .xml e o objeto Document criado a partir do mesmo\n ' prog = re.compile('(\\.xml)$') for (dirpath, dirnames, filenames) in os.walk(path): for filename in filenames: if re.search(prog, filename): path = os.path.normpath(os.path.join(dirpath, filename)) (yield (path, Document(path)))
def all_fps(path_to_dir): 'Recebe o caminho para o diretório e retorna uma lista com os caminhos\n absolutos para os arquivos que estão nele\n ' fps = [] for (dirpath, dirnames, filenames) in os.walk(path_to_dir): for filename in filenames: fps.append(os.path.normpath(os.path.join(dirpath, filename))) return fps
-5,625,930,603,436,072,000
Recebe o caminho para o diretório e retorna uma lista com os caminhos absolutos para os arquivos que estão nele
complexidade_textual.py
all_fps
lflage/complexidade_textual
python
def all_fps(path_to_dir): 'Recebe o caminho para o diretório e retorna uma lista com os caminhos\n absolutos para os arquivos que estão nele\n ' fps = [] for (dirpath, dirnames, filenames) in os.walk(path_to_dir): for filename in filenames: fps.append(os.path.normpath(os.path.join(dirpath, filename))) return fps