def run(sd, ed, vert_lines):
manual_orders = ms.testPolicy('JPM', sd, ed, 1000000)
bench_orders = ms.benchmark('JPM', sd, ed, 1000000)
manual_sim = marketismcode.compute_portvals(manual_orders)
bench_sim = marketismcode.compute_portvals(bench_orders)
manual_sim['value'] = manual_sim['value'] / manual_sim['value'][0]
bench_sim['value'] = bench_sim['value'] / bench_sim['value'][0]
long_positions = manual_sim['value'].where(
manual_orders['JPM'] > 0).dropna(how="all")
short_positions = manual_sim['value'].where(
manual_orders['JPM'] < 0).dropna(how="all")
daily_returns = (manual_sim['value'][1:] /
manual_sim['value'][:-1].values) - 1
dr_mean = daily_returns.mean()
dr_std = daily_returns.std()
cr = (manual_sim['value'][-1] / manual_sim['value'][0]) - 1
print str(sd) + ' - ' + str(ed)
print "Manual-----------------------"
print "\tCR: " + str(cr)
print "\tMean of daily returns: " + str(dr_mean)
print "\tSTD of daily returns: " + str(dr_std) + '\n'
bench_daily_returns = (bench_sim['value'][1:] /
bench_sim['value'][:-1].values) - 1
bench_dr_mean = bench_daily_returns.mean()
bench_dr_std = bench_daily_returns.std()
bench_cr = (bench_sim['value'][-1] / bench_sim['value'][0]) - 1
print "Bench-----------------------"
print "\tCR: " + str(bench_cr)
print "\tMean of daily returns: " + str(bench_dr_mean)
print "\tSTD of daily returns: " + str(bench_dr_std) + '\n'
long_positions.columns = ['Long Positions']
short_positions.columns = ['Short Positions']
manual_sim.columns = ['Manual Strategy']
bench_sim.columns = ['Benchmark']
ax = manual_sim.plot(title="Manual Strategy vs Benchmark\n" + str(sd) +
' - ' + str(ed),
color="black")
bench_sim.plot(ax=ax, color="blue")
if vert_lines:
for i in short_positions.index:
ax.axvline(x=i, c='r')
for i in long_positions.index:
ax.axvline(x=i, c='g')
ax.set_ylabel('Portfolio Value')
ax.set_xlabel('Date')
def find_best_params(self, symbol, start_date, end_date):
best_weights = [(0, 0, 0, 0, 0, 0)]
highest_value = None
i = 0
for mom_weight in [2, 3, 4, 8]:
for rsi_weight in [2, 3, 4, 8]:
for bol_weight in [2, 3, 4, 8]:
if mom_weight + rsi_weight + bol_weight == 0:
continue
print "on iter " + str(i)
self.__init__(bol_weight, mom_weight, rsi_weight)
orders = self.testPolicy(symbol, start_date, end_date,
100000)
value = marketismcode.compute_portvals(orders)
val = value.ix[-1][0]
if highest_value == val:
print "equivalent high value found!"
best_weights.append(
(bol_weight, rsi_weight, mom_weight))
print(bol_weight, rsi_weight, mom_weight)
if highest_value is None or val > highest_value:
print "highest value found!"
best_weights = [(bol_weight, rsi_weight, mom_weight)]
highest_value = val
print highest_value
print best_weights
print('------')
i += 1
print('----------')
print highest_value
print best_weights
import datetime as dt
import matplotlib.pyplot as plt
import BestPossibleStrategy
import marketismcode
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2011, 12, 31)
bps = BestPossibleStrategy.BestPossibleStrategy()
best_orders = bps.testPolicy('JPM', start_date, end_date, 100000)
best_values = marketismcode.compute_portvals(best_orders,
commission=0,
impact=0)
bench_orders = bps.benchmark('JPM', start_date, end_date, 1000)
bench_values = marketismcode.compute_portvals(bench_orders,
commission=0,
impact=0)
best_daily_returns = (best_values['value'][1:] /
best_values['value'][:-1].values) - 1
bench_daily_returns = (bench_values['value'][1:] /
bench_values['value'][:-1].values) - 1
best_dr_mean = best_daily_returns.mean()
bench_dr_mean = bench_daily_returns.mean()
best_dr_std = best_daily_returns.std()
bench_dr_std = bench_daily_returns.std()
def run(sd, ed, impact_learner, strategy_learner):
impact_orders = impact_learner.testPolicy(symbol, sd, ed, 1000000)
strategy_orders = strategy_learner.testPolicy(symbol, sd, ed, 1000000)
# manual_orders = ms.testPolicy(symbol,sd,ed,1000000)
# bench_orders = ms.benchmark(symbol, sd, ed, 1000000)
#
# manual_sim = marketismcode.compute_portvals(manual_orders,impact=impact, commission=0)
# bench_sim = marketismcode.compute_portvals(bench_orders,impact=impact, commission=0)
strategy_sim = marketismcode.compute_portvals(strategy_orders,
impact=impact,
commission=0)
impact_sim = marketismcode.compute_portvals(impact_orders,
impact=impact,
commission=0)
long_positions = strategy_sim['value'].where(
strategy_orders['JPM'] > 0).dropna(how="all")
short_positions = strategy_sim['value'].where(
strategy_orders['JPM'] < 0).dropna(how="all")
impact_long_positions = impact_sim['value'].where(
impact_orders['JPM'] > 0).dropna(how="all")
impact_short_positions = impact_sim['value'].where(
impact_orders['JPM'] < 0).dropna(how="all")
# manual_sim['value']= manual_sim['value']/manual_sim['value'][0]
# bench_sim['value']= bench_sim['value']/bench_sim['value'][0]
strategy_sim['value'] = strategy_sim['value'] / strategy_sim['value'][0]
impact_sim['value'] = impact_sim['value'] / impact_sim['value'][0]
# daily_returns = (manual_sim['value'][1:] / manual_sim['value'][:-1].values) - 1
#
# dr_mean = daily_returns.mean()
#
# dr_std = daily_returns.std()
#
# cr = (manual_sim['value'][-1] / manual_sim['value'][0]) - 1
# print str(sd) + ' - ' + str(ed)
# print "Manual-----------------------"
# print "\tCR: " + str(cr)
# print "\tMean of daily returns: " + str(dr_mean)
# print "\tSTD of daily returns: " + str(dr_std) + '\n'
#
# bench_daily_returns = (bench_sim['value'][1:] / bench_sim['value'][:-1].values) - 1
#
# bench_dr_mean = bench_daily_returns.mean()
#
# bench_dr_std = bench_daily_returns.std()
#
# bench_cr = (bench_sim['value'][-1] / bench_sim['value'][0]) - 1
# print "Bench-----------------------"
# print "\tCR: " + str(bench_cr)
# print "\tMean of daily returns: " + str(bench_dr_mean)
# print "\tSTD of daily returns: " + str(bench_dr_std) + '\n'
print "Impact: " + str(impact)
strategy_daily_returns = (strategy_sim['value'][1:] /
strategy_sim['value'][:-1].values) - 1
strategy_dr_mean = strategy_daily_returns.mean()
strategy_dr_std = strategy_daily_returns.std()
strategy_cr = (strategy_sim['value'][-1] / strategy_sim['value'][0]) - 1
print "Strategy-----------------------"
print "\tCR: " + str(strategy_cr)
print "\tMean of daily returns: " + str(strategy_dr_mean)
print "\tSTD of daily returns: " + str(strategy_dr_std) + '\n'
print "\tNum Trades: " + str(len(long_positions) + len(short_positions))
impact_daily_returns = (impact_sim['value'][1:] /
impact_sim['value'][:-1].values) - 1
impact_dr_mean = impact_daily_returns.mean()
impact_dr_std = impact_daily_returns.std()
impact_cr = (impact_sim['value'][-1] / impact_sim['value'][0]) - 1
print "Impact Strategy-----------------------"
print "\tCR: " + str(impact_cr)
print "\tMean of daily returns: " + str(impact_dr_mean)
print "\tSTD of daily returns: " + str(impact_dr_std) + '\n'
print "\tNum Trades: " + str(
len(impact_long_positions) + len(impact_short_positions))
# manual_sim.columns = ['Manual Trader']
# bench_sim.columns = ['Benchmark']
strategy_sim.columns = ['Strategy Learner Trained Without Impact']
impact_sim.columns = ['Strategy Learner Trained With Impact']
ax = impact_sim.plot(title=symbol + " Trader Performances\n" + str(sd) +
' - ' + str(ed) + '\n Impact: ' + str(impact),
color="black")
# bench_sim.plot(ax=ax, color="blue")
strategy_sim.plot(ax=ax, color="red")
# manual_sim.plot(ax=ax, color="green")
ax.set_ylabel('Portfolio Value')
ax.set_xlabel('Date')
def run(sd, ed, strategy_learner):
strategy_orders = strategy_learner.testPolicy(symbol, sd, ed, 1000000)
manual_orders = ms.testPolicy(symbol, sd, ed, 1000000)
bench_orders = ms.benchmark(symbol, sd, ed, 1000000)
manual_sim = marketismcode.compute_portvals(manual_orders,
commission=0,
impact=impact)
bench_sim = marketismcode.compute_portvals(bench_orders,
commission=0,
impact=impact)
strategy_sim = marketismcode.compute_portvals(strategy_orders,
commission=0,
impact=impact)
manual_sim['value'] = manual_sim['value'] / manual_sim['value'][0]
bench_sim['value'] = bench_sim['value'] / bench_sim['value'][0]
strategy_sim['value'] = strategy_sim['value'] / strategy_sim['value'][0]
daily_returns = (manual_sim['value'][1:] /
manual_sim['value'][:-1].values) - 1
dr_mean = daily_returns.mean()
dr_std = daily_returns.std()
cr = (manual_sim['value'][-1] / manual_sim['value'][0]) - 1
print str(sd) + ' - ' + str(ed)
print "Manual-----------------------"
print "\tCR: " + str(cr)
print "\tMean of daily returns: " + str(dr_mean)
print "\tSTD of daily returns: " + str(dr_std) + '\n'
bench_daily_returns = (bench_sim['value'][1:] /
bench_sim['value'][:-1].values) - 1
bench_dr_mean = bench_daily_returns.mean()
bench_dr_std = bench_daily_returns.std()
bench_cr = (bench_sim['value'][-1] / bench_sim['value'][0]) - 1
print "Bench-----------------------"
print "\tCR: " + str(bench_cr)
print "\tMean of daily returns: " + str(bench_dr_mean)
print "\tSTD of daily returns: " + str(bench_dr_std) + '\n'
strategy_daily_returns = (strategy_sim['value'][1:] /
strategy_sim['value'][:-1].values) - 1
strategy_dr_mean = strategy_daily_returns.mean()
strategy_dr_std = strategy_daily_returns.std()
strategy_cr = (strategy_sim['value'][-1] / strategy_sim['value'][0]) - 1
print "Strategy-----------------------"
print "\tCR: " + str(strategy_cr)
print "\tMean of daily returns: " + str(strategy_dr_mean)
print "\tSTD of daily returns: " + str(strategy_dr_std) + '\n'
manual_sim.columns = ['Manual Trader']
bench_sim.columns = ['Benchmark']
strategy_sim.columns = ['Strategy Learner']
ax = manual_sim.plot(title=symbol + " Trader Performances\n" + str(sd) +
' - ' + str(ed),
color="black")
bench_sim.plot(ax=ax, color="blue")
strategy_sim.plot(ax=ax, color="red")
ax.set_ylabel('Portfolio Value')
ax.set_xlabel('Date')