WIP (need to figure out join single index to multi-index)
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113
indicators.py
113
indicators.py
@ -21,8 +21,9 @@ Could also come up with a value that ties to the trading volume.
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import pandas as pd
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import numpy as np
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import datetime as dt
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from util import get_data, get_price_volume, plot_data, get_watchlist
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from marketsim import compute_portvals, compute_portfolio_stats, normalize_data
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# from util import get_data, get_price_volume, plot_data, get_watchlist
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from util import get_price_volume, plot_data, get_watchlist
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# from marketsim import compute_portvals, compute_portfolio_stats, normalize_data
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# import matplotlib.pyplot as plt
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# import matplotlib
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from numpy.fft import fft, ifft
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@ -403,28 +404,28 @@ def simple_bollinger_strategy(stk):
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return order_list
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def plot_against_sym(df, sym=['SPY']):
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df_temp = df.copy()
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df_sym = get_data(sym, pd.to_datetime(df_temp.index.values), addSPY=False)
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df_temp[sym[0]] = df_sym.values
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df_temp = normalize_data(df_temp)
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plot_data(df_temp)
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return df_sym
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# def plot_against_sym(df, sym=['SPY']):
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# df_temp = df.copy()
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# df_sym = get_data(sym, pd.to_datetime(df_temp.index.values), addSPY=False)
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# df_temp[sym[0]] = df_sym.values
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# df_temp = normalize_data(df_temp)
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# plot_data(df_temp)
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# return df_sym
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def test_bollinger_sell():
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sd = dt.datetime(2010,1,1)
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# ed = dt.datetime.today()
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ed = dt.datetime(2012,12,31)
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symbol = ['XOM']
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dates = dates = pd.date_range(sd, ed)
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prices = get_data(symbol, dates, addSPY=False)
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# prices = prices.dropna()
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stk = security(prices)
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sell = bollinger_sell(stk)
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plot_data(sell)
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buy = bollinger_buy(stk, 190)
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plot_data(buy)
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# def test_bollinger_sell():
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# sd = dt.datetime(2010,1,1)
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# # ed = dt.datetime.today()
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# ed = dt.datetime(2012,12,31)
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# symbol = ['XOM']
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# dates = dates = pd.date_range(sd, ed)
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# prices = get_data(symbol, dates, addSPY=False)
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# # prices = prices.dropna()
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# stk = security(prices)
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# sell = bollinger_sell(stk)
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# plot_data(sell)
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# buy = bollinger_buy(stk, 190)
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# plot_data(buy)
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def get_crossing(stocks):
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"""
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@ -515,43 +516,43 @@ def modified_bollinger_strategy(stk):
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return order_list
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def test_get_orders():
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sd = dt.datetime(2000,2,1)
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# ed = dt.datetime.today()
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ed = dt.datetime(2012,9,12)
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symbol = ['INTC', 'XOM', 'MSFT']
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dates = dates = pd.date_range(sd, ed)
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prices = get_data(symbol, dates, addSPY=False)
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stk = security(prices)
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# def test_get_orders():
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# sd = dt.datetime(2000,2,1)
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# # ed = dt.datetime.today()
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# ed = dt.datetime(2012,9,12)
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# symbol = ['INTC', 'XOM', 'MSFT']
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# dates = dates = pd.date_range(sd, ed)
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# prices = get_data(symbol, dates, addSPY=False)
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# stk = security(prices)
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# order_list = simple_bollinger_strategy(stk)
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order_list = modified_bollinger_strategy(stk)
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# # order_list = simple_bollinger_strategy(stk)
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# order_list = modified_bollinger_strategy(stk)
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# print(order_list)
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port_val = compute_portvals(order_list,100000,9.95,0.005)
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if isinstance(port_val, pd.DataFrame):
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port_val = port_val[port_val.columns[0]].to_frame() # just get the first column
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else:
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print("warning, code did not return a DataFrame")
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price_SPY = plot_against_sym(port_val)
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# # print(order_list)
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# port_val = compute_portvals(order_list,100000,9.95,0.005)
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# if isinstance(port_val, pd.DataFrame):
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# port_val = port_val[port_val.columns[0]].to_frame() # just get the first column
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# else:
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# print("warning, code did not return a DataFrame")
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# price_SPY = plot_against_sym(port_val)
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rfr=0
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sf=252
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# rfr=0
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# sf=252
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cr, adr, sddr, sr = compute_portfolio_stats(port_val, [1.0], rfr, sf)
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crSP,adrSP,sddrSP,srSP = compute_portfolio_stats(price_SPY, [1.0], rfr, sf)
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# Compare portfolio against $SPX
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print("\nDate Range: {} to {}".format(sd.date(), ed.date()))
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print()
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print("Sharpe Ratio: {}, {}".format(sr, srSP))
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print()
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print("Cumulative Return: {}, {}".format(cr, crSP))
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print()
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print("Standard Deviation: {}, {}".format(sddr, sddrSP))
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print()
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print("Average Daily Return: {}, {}".format(adr, adrSP))
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print()
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print("Final Portfolio Value: {:.2f}".format(port_val['Portfolio'].iloc[-1]))
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# cr, adr, sddr, sr = compute_portfolio_stats(port_val, [1.0], rfr, sf)
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# crSP,adrSP,sddrSP,srSP = compute_portfolio_stats(price_SPY, [1.0], rfr, sf)
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# # Compare portfolio against $SPX
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# print("\nDate Range: {} to {}".format(sd.date(), ed.date()))
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# print()
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# print("Sharpe Ratio: {}, {}".format(sr, srSP))
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# print()
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# print("Cumulative Return: {}, {}".format(cr, crSP))
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# print()
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# print("Standard Deviation: {}, {}".format(sddr, sddrSP))
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# print()
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# print("Average Daily Return: {}, {}".format(adr, adrSP))
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# print()
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# print("Final Portfolio Value: {:.2f}".format(port_val['Portfolio'].iloc[-1]))
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def plot_basic(stk, axs):
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data = stk.price.copy()
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@ -880,7 +881,7 @@ if __name__ == "__main__":
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Input(component_id='button', component_property='n_clicks'),
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prevent_initial_call=True,
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)
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def get_data(clicks):
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def get_data_cb(clicks):
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# global all_plot_sym, all_plot_ind, all_data, all_vol, all_macd, all_rsi
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# if clicks == 0:
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# return # no update
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143
util.py
143
util.py
@ -11,7 +11,8 @@ import datetime as dt
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import os
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import pandas as pd
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import numpy as np
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import yfinance as yf
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# import yfinance as yf
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import yahoo_fin.stock_info as si
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import requests
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from lxml import html
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from io import StringIO
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@ -32,19 +33,19 @@ def symbol_to_path(symbol, base_dir=None):
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base_dir = os.environ.get("MARKET_DATA_DIR", '../data/')
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return os.path.join(base_dir, "{}.csv".format(str(symbol)))
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def get_data_full(symbols, start_date, addSPY=True, colname = 'Adj Close'):
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"""
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Read stock data (adjusted close) for given symbols from Yahoo Finance
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from start_date to the latest date available (usually the current date).
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"""
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if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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symbols = ['SPY'] + symbols
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# def get_data_full(symbols, start_date, addSPY=True, colname = 'Adj Close'):
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# """
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# Read stock data (adjusted close) for given symbols from Yahoo Finance
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# from start_date to the latest date available (usually the current date).
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# """
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# if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# symbols = ['SPY'] + symbols
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df = yf.download(symbols, start = start_date)[colname]
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if len(symbols) == 1:
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df.name = symbols[0]
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df = df.to_frame()
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return df
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# df = yf.download(symbols, start = start_date)[colname]
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# if len(symbols) == 1:
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# df.name = symbols[0]
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# df = df.to_frame()
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# return df
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# def get_data_full(symbols, start_date, addSPY=True, colname = 'Adj Close'):
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"""
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@ -80,30 +81,42 @@ def get_data_range(df, dates):
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df_range = df_range.join(df, how='inner')
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return df_range
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def get_data(symbols, dates, addSPY=True, colname = 'Adj Close'):
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"""
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Read stock data (adjusted close) for given symbols from Yahoo Finance
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"""
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org_sym = symbols
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sd = dates[0]
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ed = dates[-1]
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# if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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if 'SPY' not in symbols:
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symbols = ['SPY'] + symbols
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df = yf.download(symbols, start=sd, end = ed)[colname]
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if len(symbols) == 1:
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df.name = symbols[0]
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df = df.to_frame()
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df = df.dropna(subset=['SPY'])
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df = fill_missing_data(df)
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if addSPY==False:
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# df = df.drop(columns=['SPY'])
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df = df[org_sym]
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def yf_download(symbols, start, end):
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df = pd.DataFrame(columns = pd.MultiIndex(levels=[["Adj Close", "Volume"],[]], codes=[[],[]], names=["adjclose", "volume"]))
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for sym in symbols:
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# tmp = si.get_data(sym, start_date=start)
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tmp = si.get_data(sym, start_date=start)[["adjclose", "volume"]]
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tuples = list(zip(tmp.columns.values.tolist(), \
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[symbols[0]]*len(tmp.columns.values)))
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tmp.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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df = df.join(tmp, how='outer')
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return df
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# def get_data(symbols, dates, addSPY=True, colname = 'Adj Close'):
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# """
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# Read stock data (adjusted close) for given symbols from Yahoo Finance
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# """
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# org_sym = symbols
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# sd = dates[0]
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# ed = dates[-1]
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# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# if 'SPY' not in symbols:
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# symbols = ['SPY'] + symbols
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# df = yf.download(symbols, start=sd, end = ed)[colname]
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# if len(symbols) == 1:
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# df.name = symbols[0]
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# df = df.to_frame()
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# df = df.dropna(subset=['SPY'])
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# df = fill_missing_data(df)
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# if addSPY==False:
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# # df = df.drop(columns=['SPY'])
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# df = df[org_sym]
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# return df
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def yf_batch_download(symbols, start, end, batch_size, time_gap):
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"""
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download in small batches to avoid connection closure by host
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@ -131,10 +144,10 @@ def yf_batch_download(symbols, start, end, batch_size, time_gap):
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batches = n // batch_size
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df = pd.DataFrame()
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for i in range(batches - 1):
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tmp = yf.download(symbols[i*batch_size:(i+1)*batch_size], start, end)
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tmp = yf_download(symbols[i*batch_size:(i+1)*batch_size], start, end)
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df = pd.concat([df, tmp], axis=1)
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sleep(time_gap)
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tmp = yf.download(symbols[(batches-1)*batch_size:n], start, end)
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tmp = yf_download(symbols[(batches-1)*batch_size:n], start, end)
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df = pd.concat([df, tmp], axis=1)
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return df
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@ -245,38 +258,38 @@ def get_price_volume(symbols, dates, addSPY=False):
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return price, volume
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def get_price_volume_online(symbols, dates, addSPY=False):
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"""
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Read stock data (adjusted close and volume) for given symbols from Yahoo
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Finance
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"""
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org_sym = symbols
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sd = dates[0]
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ed = dates[-1]
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# if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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if 'SPY' not in symbols:
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symbols = ['SPY'] + symbols
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df = yf.download(symbols, start=sd, end = ed)
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if len(symbols) == 1:
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df = df.dropna(subset = ['Adj Close'])
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else:
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df = df.dropna(subset=[('Adj Close', 'SPY')])
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price = df['Adj Close']
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price = fill_missing_data(price)
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volume = df['Volume']
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volume = volume.fillna(0)
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# def get_price_volume_online(symbols, dates, addSPY=False):
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# """
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# Read stock data (adjusted close and volume) for given symbols from Yahoo
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# Finance
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# """
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# org_sym = symbols
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# sd = dates[0]
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# ed = dates[-1]
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# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# if 'SPY' not in symbols:
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# symbols = ['SPY'] + symbols
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# df = yf.download(symbols, start=sd, end = ed)
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# if len(symbols) == 1:
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# df = df.dropna(subset = ['Adj Close'])
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# else:
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# df = df.dropna(subset=[('Adj Close', 'SPY')])
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# price = df['Adj Close']
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# price = fill_missing_data(price)
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# volume = df['Volume']
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# volume = volume.fillna(0)
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if len(symbols) == 1:
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price.name = symbols[0]
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volume.name = symbols[0]
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price = price.to_frame()
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volume = volume.to_frame()
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# if len(symbols) == 1:
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# price.name = symbols[0]
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# volume.name = symbols[0]
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# price = price.to_frame()
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# volume = volume.to_frame()
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if addSPY==False:
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price = price[org_sym]
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volume = volume[org_sym]
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# if addSPY==False:
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# price = price[org_sym]
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# volume = volume[org_sym]
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return price, volume
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# return price, volume
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def get_watchlist(website: str = WEBSITE):
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page = requests.get(WEBSITE)
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