Synced repo using 'sync_with_huggingface' Github Action

page/complete_backtest.py

@@ -6,6 +6,14 @@ from utils import complete_test
 from os import cpu_count
 
 def complete_backtest():
+    @st.cache_data
+    def load_data():
+        # Load data
+        limits = pd.read_csv('data/yahoo_limits.csv')
+        return limits
+
+    limits = load_data()
+
     st.markdown(
         """
         # Algorithmic Trading Dashboard
@@ -15,7 +23,6 @@ def complete_backtest():
 
     st.info("Strategy runs on most of the Nifty50 stocks",  icon="ℹ️")
 
-    limits = pd.read_csv('data/yahoo_limits.csv')
     period_list = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
 
     c1, c2 = st.columns(2)
@@ -24,7 +31,8 @@ def complete_backtest():
         strategy = st.selectbox("Select Strategy", ['Order Block', 'Order Block with EMA', 'Structure trading'], index=2)
     with c2:
         # Swing High/Low window size
-        swing_hl = st.number_input("Swing High/Low Window Size", min_value=1, value=10)
+        swing_hl = st.number_input("Swing High/Low Window Size", min_value=1, value=10,
+                                   help = "Minimum window size for finding swing highs and lows.")
 
     c1, c2 = st.columns(2)
     with c1:
@@ -39,23 +47,36 @@ def complete_backtest():
 
     # EMA parameters if "Order Block with EMA" is selected
     if strategy == "Order Block with EMA":
-        c1, c2, c3 = st.columns(3)
+        c1, c2, c3 = st.columns([2, 2, 1.5])
         with c1:
-            ema1 = st.number_input("Fast EMA Length", min_value=1, value=9)
+            ema1 = st.number_input("Fast EMA Length", min_value=1, value=9,
+                                   help = "Length of Fast moving Exponential Moving Average.")
         with c2:
-            ema2 = st.number_input("Slow EMA Length", min_value=1, value=21)
+            ema2 = st.number_input("Slow EMA Length", min_value=1, value=21,
+                                   help = "Length of Slow moving Exponential Moving Average.")
         with c3:
             cross_close = st.checkbox("Close trade on EMA crossover", value=False)
     else:
         ema1, ema2, cross_close = None, None, None
 
-    multiprocess = st.checkbox(f"Multiprocess (use {cpu_count()} CPUs)", value=True)
+    with st.expander("Advanced options"):
+        c1, c2, c3 = st.columns([2, 2, 1])
+        with c1:
+            initial_cash = st.number_input("Initial Cash [₹]", min_value=10000, value=10000)
+        with c2:
+            commission = st.number_input("Commission [%]", value = 0, min_value=-10, max_value=10,
+                                         help="Commission is the commission ratio. E.g. if your broker's "
+                                              "commission is 1% of trade value, set commission to 1.")
+        with c3:
+            multiprocess = st.checkbox(f"Multiprocess (use {cpu_count()} CPUs)", value=True)
 
     # Button to run the analysis
     if st.button("Run"):
         start = time.time()
-        st.session_state.results = complete_test(strategy, period, interval, multiprocess, swing_hl=swing_hl, ema1=ema1, ema2=ema2, cross_close=cross_close)
-        # st.write(f"Analysis finished in {time.strftime("%Hh%Mm%Ss", time.gmtime(time.time()-start))}")
+        st.session_state.results = complete_test(strategy, period, interval, multiprocess,
+                                        swing_hl=swing_hl, ema1=ema1, ema2=ema2,
+                                        cross_close=cross_close, cash=initial_cash,
+                                        commission=commission/100)
         st.success(f"Analysis finished in {round(time.time()-start, 2)} seconds")
 
     if "results" in st.session_state:
@@ -64,7 +85,7 @@ def complete_backtest():
                     ### :orange[Nifty50 stocks backtest result by using {strategy}]
                      ⬇️ Select a row in index column to get detailed information of the respective stock run.
                     """)
-        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [$]', 'Buy & Hold Return [%]', '# Trades', 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']
+        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [₹]', 'Buy & Hold Return [%]', '# Trades', 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']
         df = st.dataframe(st.session_state.results, hide_index=True, column_order=cols, on_select="rerun", selection_mode="single-row")
         df.selection.rows = 1
         if df.selection.rows:

page/single_backtest.py

@@ -1,30 +1,19 @@
 import pandas as pd
 import streamlit as st
-import os
-import random
-from bokeh.io import output_file, save
-from bokeh.plotting import figure
 from streamlit.components import v1 as components
 
 from indicators import SMC
 from utils import fetch, run_strategy
 
-def use_file_for_bokeh(chart: figure, chart_height=1067):
-    # Function used to replace st.boken_chart, because streamlit doesn't support bokeh v3
-    file_name = f'bokeh_graph_{random.getrandbits(8)}.html'
-    output_file(file_name)
-    save(chart)
-    with open(file_name, 'r', encoding='utf-8') as f:
-        html = f.read()
-    os.remove(file_name)
-    components.html(html, height=chart_height)
-
-st.bokeh_chart = use_file_for_bokeh
-
 def algorithmic_trading_dashboard():
-    # Load data
-    symbols = pd.read_csv('data/Ticker_List_NSE_India.csv')
-    limits = pd.read_csv('data/yahoo_limits.csv')
+    @st.cache_data
+    def load_data():
+        # Load data
+        symbols = pd.read_csv('data/Ticker_List_NSE_India.csv')
+        limits = pd.read_csv('data/yahoo_limits.csv')
+        return symbols, limits
+
+    symbols, limits = load_data()
 
     # Dropdown options
     period_list = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
@@ -62,20 +51,32 @@ def algorithmic_trading_dashboard():
 
     with c2:
         # Swing High/Low window size
-        swing_hl = st.number_input("Swing High/Low Window Size", min_value=1, value=10)
+        swing_hl = st.number_input("Swing High/Low Window Size", min_value=1, value=10,
+                                   help = "Minimum window size for finding swing highs and lows.")
 
     # EMA parameters if "Order Block with EMA" is selected
     if strategy == "Order Block with EMA":
         c1, c2, c3 = st.columns(3)
         with c1:
-            ema1 = st.number_input("Fast EMA Length", min_value=1, value=9)
+            ema1 = st.number_input("Fast EMA Length", min_value=1, value=9,
+                                   help = "Length of Fast moving Exponential Moving Average.")
         with c2:
-            ema2 = st.number_input("Slow EMA Length", min_value=1, value=21)
+            ema2 = st.number_input("Slow EMA Length", min_value=1, value=21,
+                                   help = "Length of Slow moving Exponential Moving Average.")
         with c3:
             cross_close = st.checkbox("Close trade on EMA crossover", value=False)
     else:
         ema1, ema2, cross_close = None, None, None
 
+    with st.expander("Advanced options"):
+        c1, c2 = st.columns(2)
+        with c1:
+            initial_cash = st.number_input("Initial Cash [₹]", min_value=10000, value=10000)
+        with c2:
+            commission = st.number_input("Commission [%]", value = 0, min_value=-10, max_value=10,
+                                         help="Commission is the commission ratio. E.g. if your broker's "
+                                              "commission is 1% of trade value, set commission to 1.")
+
     # Button to run the analysis
     if st.button("Run"):
         # Fetch ticker data
@@ -96,7 +97,9 @@ def algorithmic_trading_dashboard():
                 .update_layout(title=dict(text=ticker))
             )
 
-        backtest_results = run_strategy(ticker, strategy, period, interval, swing_hl=swing_hl, ema1=ema1, ema2=ema2, cross_close=cross_close)
+        backtest_results = run_strategy(ticker, strategy, period, interval,
+                                swing_hl=swing_hl, ema1=ema1, ema2=ema2, cross_close=cross_close,
+                                cash=initial_cash, commission=commission/100)
 
         color = "green" if backtest_results['Return [%]'].values[0] > 0 else "red"
 
@@ -105,7 +108,7 @@ def algorithmic_trading_dashboard():
         st.plotly_chart(signal_plot, width=1200)
 
         st.write(f'### :{color}[Backtest Results]')
-        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [$]', 'Buy & Hold Return [%]', '# Trades',
+        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [₹]', 'Buy & Hold Return [%]', '# Trades',
                 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']
         st.dataframe(backtest_results, hide_index=True, column_order=cols)
 

strategies.py

@@ -41,6 +41,7 @@ class SMC_test(Strategy):
 
 
 class SMC_ema(SignalStrategy, TrailingStrategy):
+    swing_window = 10
     ema1 = 9
     ema2 = 21
     close_on_crossover = False
@@ -49,8 +50,8 @@ class SMC_ema(SignalStrategy, TrailingStrategy):
         super().init()
 
         # Setting smc buy and sell indicators.
-        self.smc_b = self.I(self.smc_buy, self.data.df)
-        self.smc_s = self.I(self.smc_sell, self.data.df)
+        self.smc_b = self.I(self.smc_buy, data=self.data.df, swing_hl=self.swing_window)
+        self.smc_s = self.I(self.smc_sell, data=self.data.df, swing_hl=self.swing_window)
 
         close = self.data.Close
 

utils.py

@@ -13,21 +13,21 @@ def fetch(symbol, period, interval):
     df.columns =df.columns.get_level_values(0)
     return df
 
-def smc_backtest(data, filename, swing_hl, **kwargs):
-    bt = Backtest(data, SMC_test, **kwargs)
-    results = bt.run(swing_window=swing_hl)
+def smc_backtest(data, filename, **kwargs):
+    bt = Backtest(data, SMC_test, cash=kwargs['cash'], commission=kwargs['commission'])
+    results = bt.run(swing_window=kwargs['swing_hl'])
     bt.plot(filename=filename, open_browser=False)
     return results
 
-def smc_ema_backtest(data, filename, ema1, ema2, closecross, **kwargs):
-    bt = Backtest(data, SMC_ema, **kwargs)
-    results = bt.run(ema1=ema1, ema2=ema2, close_on_crossover=closecross)
+def smc_ema_backtest(data, filename, **kwargs):
+    bt = Backtest(data, SMC_ema, cash=kwargs['cash'], commission=kwargs['commission'])
+    results = bt.run(swing_window=kwargs['swing_hl'], ema1=kwargs['ema1'], ema2=kwargs['ema2'], close_on_crossover=kwargs['cross_close'])
     bt.plot(filename=filename, open_browser=False)
     return results
 
-def smc_structure_backtest(data, filename, swing_hl, **kwargs):
-    bt = Backtest(data, SMCStructure, **kwargs)
-    results = bt.run(swing_window=swing_hl)
+def smc_structure_backtest(data, filename, **kwargs):
+    bt = Backtest(data, SMCStructure, cash=kwargs['cash'], commission=kwargs['commission'])
+    results = bt.run(swing_window=kwargs['swing_hl'])
     bt.plot(filename=filename, open_browser=False)
     return results
 
@@ -51,11 +51,11 @@ def run_strategy(ticker_symbol, strategy, period, interval, **kwargs):
     filename = f'{ticker_symbol}.html'
 
     if strategy == "Order Block":
-        backtest_results = smc_backtest(data, filename, kwargs['swing_hl'])
+        backtest_results = smc_backtest(data, filename, **kwargs)
     elif strategy == "Order Block with EMA":
-        backtest_results = smc_ema_backtest(data, filename, kwargs['ema1'], kwargs['ema2'], kwargs['cross_close'])
+        backtest_results = smc_ema_backtest(data, filename, **kwargs)
     elif strategy == "Structure trading":
-        backtest_results = smc_structure_backtest(data, filename, kwargs['swing_hl'])
+        backtest_results = smc_structure_backtest(data, filename, **kwargs)
     else:
         raise Exception('Strategy not found')
 
@@ -75,6 +75,8 @@ def run_strategy(ticker_symbol, strategy, period, interval, **kwargs):
             'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]', 'plot']
     backtest_results = backtest_results[cols]
 
+    backtest_results = backtest_results.rename(columns = {'Equity Final [$]': 'Equity Final [₹]'})
+
     return backtest_results
 
 def complete_test(strategy: str, period: str, interval: str, multiprocess=True, **kwargs):