def evaluate(symbol, sd, ed, startVal):
ts = ManualStrategy.ManualStrategy()
trades_df = ts.testPolicy(symbol, sd, ed, startVal)
port_vals = compute_portvals(trades_df, symbol, startVal)
indices = port_vals.index.values
print "Cumulative returns manual strategy is: ", \
port_vals.ix[indices[-1], CONSTANTS.TOTAL]/port_vals.ix[indices[0], CONSTANTS.TOTAL]
port_vals_benchmark = compute_portvals(benchmark(symbol, sd, ed), symbol,
startVal)
print "Cumulative returns benchmark is: ", \
port_vals_benchmark.ix[indices[-1], CONSTANTS.TOTAL]/port_vals_benchmark.ix[indices[0], CONSTANTS.TOTAL]
ts = StrategyLearner.StrategyLearner(impact=0)
ts.addEvidence(symbol, sd, ed, startVal)
trades_df = ts.testPolicy(symbol, sd, ed, startVal)
port_vals_strategy = compute_portvals(trades_df, symbol, startVal)
print "Cumulative returns strategy learner is: ", \
port_vals_strategy.ix[indices[-1], CONSTANTS.TOTAL]/port_vals_strategy.ix[indices[0], CONSTANTS.TOTAL]
plot_df = pd.DataFrame(index=port_vals.index)
plot_df["Manual Portfolio"] = port_vals[CONSTANTS.TOTAL]
plot_df["Benchmark"] = port_vals_benchmark[CONSTANTS.TOTAL]
plot_df["Strategy Learner"] = port_vals_strategy[CONSTANTS.TOTAL]
plot_df = plot_df / plot_df.ix[plot_df.index.values[0]]
plot_df.plot(color=['red', 'green', 'blue'], linewidth=1)
plt.savefig('experiment1.png')
def evaluate(symbol, sd, ed, startVal):
port_vals_benchmark = compute_portvals(benchmark(symbol, sd, ed), symbol,
startVal)
indices = port_vals_benchmark.index.values
impact = 0
ts = StrategyLearner.StrategyLearner(impact=0)
ts.addEvidence(symbol, sd, ed, startVal)
trades_df = ts.testPolicy(symbol, sd, ed, startVal)
print "Number of trades with impact: ", impact, " is ", getNumTrades(
trades_df)
port_vals_strategy0 = compute_portvals(trades_df,
symbol,
startVal,
impact=0)
print "Cumulative returns with impact: ", impact, "are ", \
port_vals_strategy0.ix[indices[-1], CONSTANTS.TOTAL]/port_vals_strategy0.ix[indices[0], CONSTANTS.TOTAL]
impact = 0.010
ts = StrategyLearner.StrategyLearner(impact=impact)
ts.addEvidence(symbol, sd, ed, startVal)
trades_df = ts.testPolicy(symbol, sd, ed, startVal)
print "Number of trades with impact: ", impact, " is ", getNumTrades(
trades_df)
port_vals_strategy5 = compute_portvals(trades_df,
symbol,
startVal,
impact=impact)
print "Cumulative returns with impact: ", impact, "are ", \
port_vals_strategy5.ix[indices[-1], CONSTANTS.TOTAL] / port_vals_strategy5.ix[indices[0], CONSTANTS.TOTAL]
impact = 0.015
ts = StrategyLearner.StrategyLearner(impact=impact)
ts.addEvidence(symbol, sd, ed, startVal)
trades_df = ts.testPolicy(symbol, sd, ed, startVal)
print "Number of trades with impact: ", impact, " is ", getNumTrades(
trades_df)
port_vals_strategy15 = compute_portvals(trades_df,
symbol,
startVal,
impact=impact)
print "Cumulative returns with impact: ", impact, "are ", \
port_vals_strategy15.ix[indices[-1], CONSTANTS.TOTAL] / port_vals_strategy15.ix[indices[0], CONSTANTS.TOTAL]
plot_df = pd.DataFrame(index=port_vals_benchmark.index)
plot_df["Benchmark"] = port_vals_benchmark[CONSTANTS.TOTAL]
plot_df["Impact 0"] = port_vals_strategy0[CONSTANTS.TOTAL]
plot_df["Impact 10"] = port_vals_strategy5[CONSTANTS.TOTAL]
plot_df["Impact 15"] = port_vals_strategy15[CONSTANTS.TOTAL]
plot_df = plot_df / plot_df.ix[plot_df.index.values[0]]
plot_df.plot(color=['red', 'green', 'blue', 'black'], linewidth=1)
plt.savefig('experiment2.png')
def evaluateInSample():
ts = ManualStrategy()
trades_df = ts.testPolicy(CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.IN_SAMPLE_START,
CONSTANTS.IN_SAMPLE_END, CONSTANTS.START_CASH)
port_vals = marketsim.compute_portvals(trades_df, CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.START_CASH)
port_vals_benchmark = marketsim.compute_portvals(
benchmark(CONSTANTS.DEFAULT_SYMBOL, CONSTANTS.IN_SAMPLE_START,
CONSTANTS.IN_SAMPLE_END), CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.START_CASH)
plot_df = pd.DataFrame(index=port_vals.index)
plot_df["Manual Portfolio"] = port_vals[CONSTANTS.TOTAL]
plot_df["Benchmark"] = port_vals_benchmark[CONSTANTS.TOTAL]
plot_df = plot_df / plot_df.ix[plot_df.index.values[0]]
plot_df.plot(color=['red', 'green'], linewidth=1)
date_vals = trades_df.index.values
previous_holdings = 0
for index, current_date in enumerate(date_vals):
current_holdings = previous_holdings + trades_df.ix[current_date,
CONSTANTS.TRADES]
if previous_holdings != current_holdings:
if current_holdings > 0:
plt.axvline(current_date, color='blue')
elif current_holdings < 0:
plt.axvline(current_date, color='black')
previous_holdings = current_holdings
plt.savefig('Manual-InSample.png')
print "In Sample"
portfolio_values_final = port_vals.sum(axis=1)
portfolio_values_benchmark_final = port_vals_benchmark.sum(axis=1)
daily_rets = get_daily_returns(portfolio_values_final)
daily_rets_benchmark = get_daily_returns(portfolio_values_benchmark_final)
print "Cumulative returns: ", get_cumm_returns(
portfolio_values_benchmark_final), get_cumm_returns(
portfolio_values_final)
print "Stddev: ", get_std_daily_returns(
daily_rets_benchmark), get_std_daily_returns(daily_rets)
print "Mean of daily returns: ", get_avg_daily_returns(
daily_rets_benchmark), get_avg_daily_returns(daily_rets)
return daily_returns.mean()
def get_std_daily_returns(daily_returns):
return daily_returns.std()
if __name__ == "__main__":
ts = TheoreticallyOptimalStrategy()
trades_df = ts.testPolicy(CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.IN_SAMPLE_START,
CONSTANTS.IN_SAMPLE_END, CONSTANTS.START_CASH)
port_vals = marketsim.compute_portvals(trades_df, CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.START_CASH, 0, 0)
port_vals_benchmark = marketsim.compute_portvals(
benchmark(CONSTANTS.DEFAULT_SYMBOL, CONSTANTS.IN_SAMPLE_START,
CONSTANTS.IN_SAMPLE_END), CONSTANTS.DEFAULT_SYMBOL,
CONSTANTS.START_CASH)
plot_df = pd.DataFrame(index=port_vals.index)
plot_df["Optimal Portfolio"] = port_vals[CONSTANTS.TOTAL]
plot_df["Benchmark"] = port_vals_benchmark[CONSTANTS.TOTAL]
plot_df = plot_df / plot_df.ix[plot_df.index.values[0]]
plot_df.plot(colors=['red', 'green'])
plt.savefig('TOP.png')
portfolio_values_final = port_vals.sum(axis=1)
portfolio_values_benchmark_final = port_vals_benchmark.sum(axis=1)
daily_rets = get_daily_returns(portfolio_values_final)
daily_rets_benchmark = get_daily_returns(portfolio_values_benchmark_final)
print "Cumulative returns: ", get_cumm_returns(