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indicators.py
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1023
indicators.py
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163
marketsim.py
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marketsim.py
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Feb 5 21:56:42 2020
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@author: cpan
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"""
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"""MC2-P1: Market simulator."""
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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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# import os
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from util import get_data, plot_data
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def normalize_data(df):
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return df/df.iloc[0,:]
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def fill_missing_values(df_data):
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'''First fill forward and then fill backward'''
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df_data.fillna(method="ffill", inplace=True)
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df_data.fillna(method="bfill", inplace=True)
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def get_orders(orders_file):
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if isinstance(orders_file, pd.DataFrame):
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# orders_df = orders_file.set_index('Date')
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orders_df = orders_file
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else:
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orders_df = pd.read_csv(orders_file, index_col = 'Date', parse_dates = True,
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na_values = ['nan'])
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orders_df = orders_df.dropna()
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orders_df = orders_df.sort_index()
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return orders_df
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def compute_daily_returns(df):
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daily_returns = df.copy()
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daily_returns[1:] = (df[1:] / df[:-1].values) - 1
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daily_returns.iloc[0, :] = 0
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return daily_returns
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def compute_portfolio_stats(price, allocs=[0.1,0.2,0,3,0.4], rfr=0.0, sf=252.0):
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norm_price = normalize_data(price)
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norm_positions_val = norm_price * allocs
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if len(norm_positions_val.columns) == 1:
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norm_portfolio_val = norm_positions_val
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else:
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norm_portfolio_val = norm_positions_val.sum(axis=1).to_frame('PORTFOLIO')
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cr = norm_portfolio_val.iloc[-1] / norm_portfolio_val.iloc[0] -1
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daily_returns = compute_daily_returns(norm_portfolio_val)
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daily_returns = daily_returns[1:] # remove first row (all zeros)
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adr = daily_returns.mean()
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sddr = daily_returns.std()
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sr = np.sqrt(sf) * (adr - rfr)/sddr
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return cr, adr, sddr, sr
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def plot_against_SPY(df):
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df_temp = df.copy()
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if 'SPY' not in df_temp.columns:
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df_SPY = get_data(['SPY'], pd.to_datetime(df_temp.index.values))
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df_temp['SPY'] = df_SPY.values
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else:
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df_SPY = df_temp['SPY']
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df_temp = normalize_data(df_temp)
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plot_data(df_temp)
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return df_SPY
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def compute_portvals(orders_file = "./orders/orders.csv", start_val = 1000000,
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commission=9.95, impact=0.005):
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#read in order data
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orders_df = get_orders(orders_file)
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#scan symbols
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symbols = list(set(orders_df['Symbol'].values))
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#get date range
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dates = pd.date_range(orders_df.index.values[0], orders_df.index.values[-1])
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#read in prices
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prices = get_data(symbols, dates)
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# fill_missing_values(prices) # included in get_data() function
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prices = prices[symbols]
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#add an extra column 'Cash' and initialize it to all ones
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prices['Cash'] = np.ones(prices.shape[0])
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#duplicate price df into a units df, intialize it to all zeros
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positions=prices*0.0
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#initialize cash position with starting value
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positions.loc[positions.index.values[0],['Cash']]=start_val
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#adjust positions to show how stock units and cash are changing over time with orders
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for index, row in orders_df.iterrows():
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stock_sym = row['Symbol']
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order_price = prices.loc[index, stock_sym]
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order_shrs = row['Shares']
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if row['Order'].upper() == 'BUY':
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if positions.loc[index, 'Cash'] < order_shrs*order_price +\
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commission + order_shrs*order_price*impact:
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# print('Not enough cash to excute the order:\n', row)
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pass
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else:
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#update positions on transaction days
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positions.loc[index, stock_sym] += order_shrs
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positions.loc[index, "Cash"] -= order_shrs*order_price
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#deduct commission
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positions.loc[index,"Cash"] -= commission
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#impact = no. of orders in transaction * price of each share * impact.
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positions.loc[index,"Cash"] -= order_shrs*order_price*impact
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elif row['Order'].upper() == 'SELL':
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if positions.loc[index, stock_sym] < order_shrs:
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# print('Not enough shares to sell to fill the order:\n', row)
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pass
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else:
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positions.loc[index, stock_sym] -= order_shrs
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positions.loc[index, "Cash"] += order_shrs*order_price
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#deduct commission
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positions.loc[index,"Cash"] -= commission
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#impact = no. of orders in transaction * price of each share * impact.
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positions.loc[index,"Cash"] -= order_shrs*order_price*impact
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# propagate positions beyond transaction days
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start_row = positions.index.get_loc(index) + 1
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positions.iloc[start_row:, :] = positions.iloc[start_row-1].values
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#calculate port_vals
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port_vals=prices*positions
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port_vals.insert(0, 'Portfolio', port_vals.sum(axis=1))
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return port_vals
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def test_code():
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of = "./orders/orders-05.csv"
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sv = 1000000
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# Process orders
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portvals = compute_portvals(orders_file = of, start_val = sv)
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if isinstance(portvals, pd.DataFrame):
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portvals = portvals[portvals.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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# Get portfolio stats
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start_date = pd.to_datetime(portvals.index.values[0])
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end_date = pd.to_datetime(portvals.index.values[-1])
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price_SPY = plot_against_SPY(portvals)
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#portfolio stats calculated similar to assess_portfolio
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rfr=0
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sf=252
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cr, adr, sddr, sr = compute_portfolio_stats(portvals, [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(start_date.date(), end_date.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(portvals['Portfolio'].iloc[-1]))
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if __name__ == "__main__":
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test_code()
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371
util.py
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util.py
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"""
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Use Yahoo Finance data
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"""
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import warnings
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# Suppress FutureWarnings
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warnings.simplefilter(action='ignore', category=FutureWarning)
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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 requests
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from lxml import html
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from io import StringIO
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from time import sleep
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WEBSITE = 'https://www.isolo.org/dokuwiki/knowledge_base/investing/watchlist'
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BATCHSIZE = 20
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TIMEGAP = 0.2
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def fill_missing_data(df):
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temp = df.ffill()
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temp = temp.bfill()
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return temp
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def symbol_to_path(symbol, base_dir=None):
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"""Return CSV file path given ticker symbol."""
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if base_dir is 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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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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Read stock data (adjusted close) for given symbols from CSV files
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from start_date to the latest date available in the CSV files.
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"""
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# df_temp = pd.read_csv(symbol_to_path('SPY'), index_col='Date',
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# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
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# df_temp = df_temp.rename(columns={colname: 'SPY'})
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# end_date = df_temp.index.values[-1]
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# dates = pd.date_range(start_date, end_date)
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# df = pd.DataFrame(index=dates)
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# df = df.join(df_temp)
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# df = df.dropna()
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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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# for symbol in symbols:
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# df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
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# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
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# df_temp = df_temp.rename(columns={colname: symbol})
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# df = df.join(df_temp)
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# # if symbol == 'SPY': # drop dates SPY did not trade
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# # df = df.dropna(subset=["SPY"])
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# if not addSPY:
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# df = df[symbols]
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# return df
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def get_data_range(df, dates):
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"""
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Extract sections of the data in the dates range from the full data set
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"""
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df_range = pd.DataFrame(index=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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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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Parameters
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----------
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symbols : list
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stock symbols.
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start : datetime
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start date.
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end : datetime
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stop date.
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batch_size : integer
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batch size.
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time_gap : float
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in seconds or fraction of seconds.
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Returns
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-------
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df : dataframe
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stock price volume information.
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"""
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n = len(symbols)
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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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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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df = pd.concat([df, tmp], axis=1)
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return df
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def get_price_volume(symbols, dates, addSPY=False):
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"""
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Read stock data (adjusted close and volume) for given symbols from local
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file unless data is not in local. It only gets date from Yahoo Finance
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when necessary to increase speed and reduce internet data.
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It will refresh local data if the symbols are on the _refresh.csv. This
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is necessary when stock splits, spins off or something else happens.
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"""
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# DATAFILE = "_stkdata.pickle"
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# REFRESH = "_refresh.csv"
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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_batch_download(symbols, start=sd, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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if len(symbols) == 1:
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tuples = list(zip(df.columns.values.tolist(), \
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[symbols[0]]*len(df.columns.values)))
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df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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# if not os.path.exists(DATAFILE):
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# df = yf_batch_download(symbols, start=sd, end=ed, \
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# batch_size=BATCHSIZE, time_gap=TIMEGAP)
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# if len(symbols) == 1:
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# tuples = list(zip(df.columns.values.tolist(), \
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# [symbols[0]]*len(df.columns.values)))
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# df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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# else:
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# df = pd.read_pickle(DATAFILE)
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# exist_syms = df["Adj Close"].columns.values.tolist()
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# if os.path.exists(REFRESH):
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# try:
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# refresh_df = pd.read_csv(REFRESH, header=None)
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# refresh_syms = refresh_df.values.tolist()
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# refresh_syms = [x for sublist in refresh_syms for x in sublist]
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# remove_syms = [x for x in exist_syms if x in refresh_syms]
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# if remove_syms:
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# df.drop(columns=remove_syms, axis=1, level=1, inplace=True)
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# exist_syms = [x for x in exist_syms if x not in refresh_syms]
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# except:
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# pass
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exist_syms = []
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last_day = pd.to_datetime(df.index.values[-1])
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first_day = pd.to_datetime(df.index.values[0])
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intersect_syms = list(set(org_sym) & set(exist_syms))
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# reduce df to only contain intersect_syms
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df = df.loc[:, (slice(None), intersect_syms)]
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if sd < first_day:
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# fill gap from online
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tmp_df = yf_batch_download(intersect_syms, start=sd, end=first_day, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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df = pd.concat([tmp_df, df])
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if ed >= last_day:
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# fill gap from online incl last two days to get mkt close data
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if ed.date() == last_day.date():
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tmp_df = yf_batch_download(intersect_syms, start=ed, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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else:
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tmp_df = yf_batch_download(intersect_syms, start=last_day, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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df = pd.concat([df[:-1], tmp_df])
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# get data online when new stks were added
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new_stks = np.setdiff1d(symbols, exist_syms).tolist()
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if not new_stks == []:
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tmp_df = yf_batch_download(new_stks, start=sd, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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if len(new_stks) == 1:
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tuples = list(zip(tmp_df.columns.values.tolist(), \
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[new_stks[0]]*len(tmp_df.columns.values)))
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tmp_df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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df = df.join(tmp_df)
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# df.to_pickle(DATAFILE) # save to local, overwrite existing file
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# if os.path.exists(REFRESH):
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# with open(REFRESH, 'w'):
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# pass
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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 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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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:
|
||||
price.name = symbols[0]
|
||||
volume.name = symbols[0]
|
||||
price = price.to_frame()
|
||||
volume = volume.to_frame()
|
||||
|
||||
if addSPY==False:
|
||||
price = price[org_sym]
|
||||
volume = volume[org_sym]
|
||||
|
||||
return price, volume
|
||||
|
||||
def get_watchlist(website: str = WEBSITE):
|
||||
page = requests.get(WEBSITE)
|
||||
# page = requests.get(WEBSITE, verify=False) # skip certificate check for https
|
||||
tree = html.fromstring(page.content)
|
||||
watchlist = tree.xpath('//*[@id="dokuwiki__content"]/div[1]/div/div[3]/div/pre/text()')[0]
|
||||
file_name = StringIO(watchlist)
|
||||
df = pd.read_csv(file_name, index_col = 'Symbol',
|
||||
comment = '#', na_filter=False)
|
||||
return df
|
||||
|
||||
# def get_watchlist(file_name: str = 'watchlist.csv'):
|
||||
# df = pd.read_csv(file_name, index_col = 'Symbol',
|
||||
# comment = '#', na_filter=False)
|
||||
# return df
|
||||
|
||||
# def get_data(symbols, dates, addSPY=True, colname = 'Adj Close'):
|
||||
# """
|
||||
# Read stock data (adjusted close) for given symbols from CSV files.
|
||||
|
||||
# (done) TODO: there are nan values in the data when addSPY=False is passed. The
|
||||
# strategy should be using SPY to clean the data first including fill
|
||||
# forward and fill backward, then to drop the SPY if addSPY=False
|
||||
# """
|
||||
# org_sym = symbols
|
||||
# df = pd.DataFrame(index=dates)
|
||||
# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
|
||||
# # symbols = ['SPY'] + symbols
|
||||
# if 'SPY' not in symbols:
|
||||
# symbols = ['SPY'] + symbols
|
||||
# for symbol in symbols:
|
||||
# df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
|
||||
# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
|
||||
# df_temp = df_temp.rename(columns={colname: symbol})
|
||||
# df = df.join(df_temp)
|
||||
# if symbol == 'SPY': # drop dates SPY did not trade
|
||||
# df = df.dropna(subset=["SPY"])
|
||||
# # fill missing data
|
||||
# df = fill_missing_data(df)
|
||||
# if addSPY == False: # drop SPY
|
||||
# # df = df.drop(columns=['SPY'])
|
||||
# df = df[org_sym]
|
||||
|
||||
# return df
|
||||
|
||||
|
||||
def plot_data(df, axs=[], title=[], xlabel='', ylabel=''):
|
||||
|
||||
"""Plot stock prices with a custom title and meaningful axis labels."""
|
||||
if axs == []:
|
||||
ax = df.plot(title = title)
|
||||
else:
|
||||
ax = df.plot(ax=axs, title=title)
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel(ylabel)
|
||||
ax.grid()
|
||||
|
||||
|
||||
# def plot_data(df, title=[], xlabel='', ylabel=''):
|
||||
# import matplotlib.pyplot as plt
|
||||
# """Plot stock prices with a custom title and meaningful axis labels."""
|
||||
# ax = df.plot(title=title, fontsize=12, figsize=(10, 7))
|
||||
# ax.set_xlabel(xlabel)
|
||||
# ax.set_ylabel(ylabel)
|
||||
# plt.grid()
|
||||
# plt.show()
|
||||
|
||||
def get_orders_data_file(basefilename):
|
||||
return open(os.path.join(os.environ.get("ORDERS_DATA_DIR",'orders/'),basefilename))
|
||||
|
||||
def get_learner_data_file(basefilename):
|
||||
return open(os.path.join(os.environ.get("LEARNER_DATA_DIR",'Data/'),basefilename),'r')
|
||||
|
||||
def get_robot_world_file(basefilename):
|
||||
return open(os.path.join(os.environ.get("ROBOT_WORLDS_DIR",'testworlds/'),basefilename))
|
||||
|
||||
|
||||
def test_code():
|
||||
|
||||
symbol = ['GOOG', 'AMZN']
|
||||
# lookback years
|
||||
lb_year = 0.08
|
||||
ed = dt.datetime.today()
|
||||
sd = ed - dt.timedelta(days = 365 * lb_year + 1)
|
||||
# If ed or sd falls on to a non-trading day, you might get warnings saying
|
||||
# "No data found for this date range, symbol may be delisted". This is
|
||||
# normal behavior.
|
||||
prices, volume = get_price_volume(symbol, pd.date_range(sd, ed), addSPY=False)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_code()
|
||||
Loading…
x
Reference in New Issue
Block a user