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import datetime
import gradio as gr
import pandas as pd
import yfinance as yf
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from datetime import date, timedelta
from matplotlib import pyplot as plt
from plotly.subplots import make_subplots
from pytickersymbols import PyTickerSymbols
from statsmodels.tsa.arima.model import ARIMA
from pandas.plotting import autocorrelation_plot
from dateutil.relativedelta import relativedelta
index_options = ['FTSE 100(UK)', 'NASDAQ(USA)', 'CAC 40(FRANCE)']
ticker_dict = {'FTSE 100(UK)': 'FTSE 100', 'NASDAQ(USA)': 'NASDAQ 100', 'CAC 40(FRANCE)': 'CAC 40'}
time_intervals = ['1d', '1m', '5m', '15m', '60m']
global START_DATE, END_DATE
END_DATE = date.today()
START_DATE = END_DATE - relativedelta(years=1)
FORECAST_PERIOD = 7
demo = gr.Blocks()
stock_names = []
with demo:
d1 = gr.Dropdown(index_options, label='Please select Index...', info='Will be adding more indices later on', interactive=True)
d2 = gr.Dropdown([], label='Please Select Stock from your selected index', interactive=True)
d3 = gr.Dropdown(time_intervals, label='Select Time Interval', value='1d', interactive=True)
d4 = gr.Radio(['Line Graph', 'Candlestick Graph'], label='Select Graph Type', value='Line Graph', interactive=True)
d5 = gr.Dropdown(['ARIMA', 'Prophet', 'LSTM'], label='Select Forecasting Method', value='ARIMA', interactive=True)
def forecast_series(series, model="ARIMA", forecast_period=7):
predictions = list()
if series.shape[1] > 1:
series = series['Close'].values.tolist()
if model == "ARIMA":
for i in range(forecast_period):
model = ARIMA(series, order=(5, 1, 0))
model_fit = model.fit()
output = model_fit.forecast()
yhat = output[0]
predictions.append(yhat)
series.append(yhat)
elif model == "Prophet":
# Implement Prophet forecasting method
pass
elif model == "LSTM":
# Implement LSTM forecasting method
pass
return predictions
def is_business_day(a_date):
return a_date.weekday() < 5
def get_stocks_from_index(idx):
stock_data = PyTickerSymbols()
index = ticker_dict[idx]
stocks = list(stock_data.get_stocks_by_index(index))
stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks]
return gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True)
d1.input(get_stocks_from_index, d1, d2)
def get_stock_graph(idx, stock, interval, graph_type, forecast_method):
stock_name, ticker_name = stock.split(":")
if ticker_dict[idx] == 'FTSE 100':
ticker_name += '.L' if ticker_name[-1] != '.' else 'L'
elif ticker_dict[idx] == 'CAC 40':
ticker_name += '.PA'
series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
series = series.reset_index()
predictions = forecast_series(series, model=forecast_method)
last_date = pd.to_datetime(series['Date'].values[-1])
forecast_week = []
i = 1
while len(forecast_week) < FORECAST_PERIOD:
next_date = last_date + timedelta(days=i)
if is_business_day(next_date):
forecast_week.append(next_date)
i += 1
# Ensure predictions and forecast_week have the same length
predictions = predictions[:len(forecast_week)]
forecast_week = forecast_week[:len(predictions)]
forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions})
if graph_type == 'Line Graph':
fig = go.Figure()
fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical'))
fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
else: # Candlestick Graph
fig = go.Figure(data=[go.Candlestick(x=series['Date'],
open=series['Open'],
high=series['High'],
low=series['Low'],
close=series['Close'],
name='Historical')])
fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
fig.update_layout(title=f"Stock Price of {stock_name}",
xaxis_title="Date",
yaxis_title="Price")
return fig
out = gr.Plot()
inputs = [d1, d2, d3, d4, d5]
d2.input(get_stock_graph, inputs, out)
d3.input(get_stock_graph, inputs, out)
d4.input(get_stock_graph, inputs, out)
d5.input(get_stock_graph, inputs, out)
demo.launch() |