/
simulate.py
49 lines (43 loc) · 1.79 KB
/
simulate.py
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import numpy as np
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.qsdateutil as du
import QSTK.qstkutil.tsutil as tsu
import datetime as dt
import math
def run(dt_start, dt_end, ls_symbols, alloc):
dt_timeofday = dt.timedelta(hours=16)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
c_dataobj = da.DataAccess('Yahoo')
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
na_price = d_data['close'].values
na_normalized_prices = na_price / na_price[0, :]
na_daily_returns = tsu.returnize0(na_normalized_prices.copy())
na_allocated_daily_ret = na_daily_returns * alloc
print 'Allocated-Adjusted Daily Returns'
print na_allocated_daily_ret
na_total_daily_ret = np.sum(na_allocated_daily_ret, 1)
print 'Total Daily Returns: '
print na_total_daily_ret
std_dev = np.std(na_total_daily_ret)
print 'Standard Deviation (Vol): ' + str(std_dev)
avg_daily_ret = np.average(na_total_daily_ret)
print 'Average Daily Return: ' + str(avg_daily_ret)
sharpe = calc_sharpe_ratio(avg_daily_ret, std_dev)
print 'Sharpe: ' + str(sharpe)
cum_ret = calc_cum_return(na_total_daily_ret)
print 'Cumulative Return: ' + str(cum_ret)
return std_dev, avg_daily_ret, sharpe, cum_ret
def calc_std_dev(population):
return np.std(population)
def calc_avg_daily_return(returns):
avg_daily_return = 1
for i in range(len(returns)):
avg_daily_return *= 1 + returns[i]
return avg_daily_return - 1
def calc_sharpe_ratio(avg_daily_ret, std_dev):
k = math.sqrt(250)
return k * avg_daily_ret / std_dev
def calc_cum_return(returns):
return reduce(lambda x, y: (1+x) * (1+y) - 1, returns)