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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'}
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([]) # for specific stocks
# d3 = gr.Dropdown(['General News'])
def forecast_series(series, model="ARIMA", forecast_period=7):
predictions = list()
if series.shape[1] > 1:
series = series['Close'].values.tolist()
plt.show()
if model == "ARIMA":
## Do grid search here --> Custom for all stocks
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)
return predictions
def is_business_day(a_date):
return a_date.weekday() < 5
def get_stocks_from_index(idx):
stock_data = PyTickerSymbols()
# indices = stock_data.get_all_indices()
index = ticker_dict[idx]
stock_data = PyTickerSymbols()
# returns 2d list with the following information
# 'name', 'symbol', 'country', 'indices', 'industries', 'symbols', 'metadata', 'isins', 'akas'
stocks = list(stock_data.get_stocks_by_index(index)) ##converting filter object to list
stock_names = []
for stock in stocks:
stock_names.append(stock['name'] + ':' + stock['symbol'])
d2 = gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True)
return d2
d1.input(get_stocks_from_index, d1, d2)
out = gr.Plot(every=10)
def get_stock_graph(idx, stock):
stock_name = stock.split(":")[0]
ticker_name = stock.split(":")[1]
if ticker_dict[idx] == 'FTSE 100':
if ticker_name[-1] == '.':
ticker_name += 'L'
else:
ticker_name += '.L'
elif ticker_dict[idx] == 'CAC 40':
ticker_name += '.PA'
## Can also download lower interval data apparently using line below
# data = yf.download(tickers="MSFT", period="5d", interval="1m")
series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE) # stock.split(":")[1]
series = series.reset_index()
predictions = forecast_series(series)
last_date = pd.to_datetime(series['Date'].values[-1])
forecast_week = []
while len(forecast_week) != FORECAST_PERIOD:
if is_business_day(last_date):
forecast_week.append(last_date)
last_date += timedelta(days=1)
forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions})
fig = plt.figure(figsize=(14, 5))
sns.set_style("ticks")
sns.lineplot(data=series, x="Date", y="Close", color="firebrick")
sns.lineplot(data=forecast, x="Date", y="Forecast", color="blue")
sns.despine()
plt.title("Stock Price of {}".format(stock_name), size='x-large', color='blue') # stock.split(":")[0]
text = "Your stock is:" + str(stock)
return fig
d2.input(get_stock_graph, [d1, d2], out)
demo.launch() |