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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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requirements.txt
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requirements.txt
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