def run_strategy(start_date, end_date):
print('')
#ms = manualStrategy(symbol = 'JPM', sd=start_date, ed=end_date, sv = 100000)
np.random.seed(106348)
symbols = ['AAPL', 'XOM', 'GOOG', 'IBM']
for sym in symbols:
print sym
learner = StrategyLearner()
learner.addEvidence(symbol=sym,
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=100000)
df_trades = learner.testPolicy(symbol=sym,
sd=start_date,
ed=end_date,
sv=100000)
df_orders = learner.createOrdersDataFrame(df_trades)
ms = manual.manualStrategy(symbol=sym,
sd=start_date,
ed=end_date,
sv=100000)
df_trades_ms = ms.testPolicy()
df_orders_ms = ms.createOrdersDataFrame(df_trades_ms)
benchmark = learner.createBenchMarkDataFrame(df_trades,
sd=start_date)
portvals_strategy = msim.compute_portvals(orders_file=df_orders,
start_val=100000,
commission=0,
impact=0.005,
end_date=end_date,
start_date=start_date)
portvals_benchmark = msim.compute_portvals(orders_file=benchmark,
start_val=100000,
commission=0,
impact=0,
end_date=end_date,
start_date=start_date)
portvals_manual = msim.compute_portvals(orders_file=df_orders_ms,
start_val=100000,
commission=0,
impact=0.005,
end_date=end_date,
start_date=start_date)
print('Statistics for Random Forest Strategy')
msim.computeStatistics(portvals_strategy)
print('')
print('Statistics for Manual Strategy')
msim.computeStatistics(portvals_manual)
print('')
print('Statistics for Benchmark Strategy')
msim.computeStatistics(portvals_benchmark)
generatePerformanceChart(df_trades, portvals_strategy,
portvals_manual, portvals_benchmark)
def run_strategy(start_date, end_date):
print('')
#ms = manualStrategy(symbol = 'JPM', sd=start_date, ed=end_date, sv = 100000)
np.random.seed(106348)
learner = StrategyLearner()
learner.addEvidence(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=100000)
df_trades = learner.testPolicy(symbol="JPM",
sd=start_date,
ed=end_date,
sv=100000)
df_orders = learner.createOrdersDataFrame(df_trades)
ms = manual.manualStrategy(symbol='JPM',
sd=start_date,
ed=end_date,
sv=100000)
df_trades_ms = ms.testPolicy()
df_orders_ms = ms.createOrdersDataFrame(df_trades_ms)
benchmark = learner.createBenchMarkDataFrame(df_trades, sd=start_date)
portvals_strategy = msim.compute_portvals(orders_file=df_orders,
start_val=100000,
commission=0,
impact=0.005,
end_date=end_date,
start_date=start_date)
portvals_benchmark = msim.compute_portvals(orders_file=benchmark,
start_val=100000,
commission=0,
impact=0,
end_date=end_date,
start_date=start_date)
portvals_manual = msim.compute_portvals(orders_file=df_orders_ms,
start_val=100000,
commission=0,
impact=0.005,
end_date=end_date,
start_date=start_date)
print('Statistics for Random Forest Strategy')
msim.computeStatistics(portvals_strategy)
print('')
print('Statistics for Manual Strategy')
msim.computeStatistics(portvals_manual)
print('')
print('Statistics for Benchmark Strategy')
msim.computeStatistics(portvals_benchmark)
generatePerformanceChart(df_trades, portvals_strategy, portvals_manual,
portvals_benchmark)
tot_cum_ret = 0
tot_sharpe_ratio = 0
tot_std_daily_ret = 0
tot_avg_daily_ret = 0
N = 10
i = 0
print ''
print 'Lets run the Random Forest 10 times and average results for performance'
while i < N:
np.random.seed(np.random.randint(0, 100000))
learner = StrategyLearner()
learner.addEvidence(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=100000)
df_trades = learner.testPolicy(symbol="JPM",
sd=start_date,
ed=end_date,
sv=100000)
df_orders = learner.createOrdersDataFrame(df_trades)
portvals_strategy = msim.compute_portvals(orders_file=df_orders,
start_val=100000,
commission=0,
impact=0.005,
end_date=end_date,
start_date=start_date)
cum_ret, sharpe_ratio, std_daily_ret, avg_daily_ret = msim.computeStatisticsReturnValues(
portvals_strategy)
tot_cum_ret = tot_cum_ret + cum_ret
tot_sharpe_ratio = tot_sharpe_ratio + sharpe_ratio
tot_std_daily_ret = tot_std_daily_ret + std_daily_ret
i = i + 1
print 'The average cumulative return is: ' + str(tot_cum_ret / N)
print 'The average sharpe ratio is: ' + str(tot_sharpe_ratio / N)
print 'The average standard dev of daily returns is: ' + str(
tot_std_daily_ret / N)