)
# Update shadow trades
new_st_df = shadow_trades.append({'Date' :tradeDate,
'signal' :y_validate[0],
'gainAhead':0.000,
'Close' :feat_df.Close[lastRow-1]}
,ignore_index=True
)
new_st_df['gainAhead'] = ct.gainAhead(new_st_df.Close)
# save updated shadow trades
dSet.save_csv(system_directory,
system_name,
'OOS_Equity',
'dbd',
new_st_df
)
# Load TMS Part 1
tms1 = dSet.read_csv(system_directory,
system_name,
'TMS_Part1',
'dbd'
)
# Update TMS-Part 1 data with latest date
sst = tms1.append({'Date' :tradeDate,
'signal' :y_validate[0],
'gainAhead' :0,
'Close' :feat_df.Close[lastRow-1]}
sst1.iloc[i - 1, sst1.columns.get_loc('maxEquity')])
#sst1.drawdown[i] = (sst1.maxEquity[i]-sst1.equity[i]) / sst1.maxEquity[i]
sst1.iloc[i, sst1.columns.get_loc('drawdown')] = (
sst1.iloc[i, sst1.columns.get_loc('maxEquity')] -
sst1.iloc[i, sst1.columns.get_loc('equity')]
) / sst1.iloc[i, sst1.columns.get_loc('maxEquity')]
#sst1.maxDD[i] = max(sst1.drawdown[i],sst1.maxDD[i-1])
sst1.iloc[i, sst1.columns.get_loc('maxDD')] = max(
sst1.iloc[i, sst1.columns.get_loc('drawdown')],
sst1.iloc[i - 1, sst1.columns.get_loc('maxDD')])
# Show a few items
print(sst1.tail(2))
sst1.reset_index(level='Date', inplace=True)
dSet.save_csv(system_directory, system_name, 'TMS_Part2', 'dbd', sst1)
plot_tms = sst1.set_index(pd.DatetimeIndex(sst1['Date']))
plot_tms = plot_tms.drop('Date', axis=1)
# Plot the equity curve and drawdown
plotIt.plot_equity_drawdown(issue, plot_tms)
# plot_tms = sst1.set_index(pd.DatetimeIndex(sst1['Date']))
# plot_tms=plot_tms.drop('Date', axis=1)
# plotTitle = "Equity curve for " + issue
# plotIt.plot_v1(plot_tms['equity'][:-2], plotTitle)
# plotTitle = "Drawdown for " + issue
# plotIt.plot_v1(plot_tms['drawdown'][:-2], plotTitle)
# plt.show()
'EndDate',
'Model',
'Rows',
'beLongCount',
'Features',
'FeatureCount',
'Train-Accuracy',
'Train-Precision',
'Train-RMC',
'Train-RF',
'Train-NPV',
'Train-MCC',
'Train-EV',
'Test-Accuracy',
'Test-Precision',
'Test-RMC',
'Test-RF',
'Test-NPV',
'Test-MCC',
'Test-EV',
]]
#print(df)
# Save results
dSet.save_csv(system_directory,
system_name,
'IS_Equity',
'new',
df
)
print('Total value of Losses: %.2f' % tradeLossesValue)
#(Win % x Average Win Size) – (Loss % x Average Loss Size)
print('Expectancy: %.2f' %
((tradeWins / numberTrades) * (tradeWinsValue / tradeWins) -
(tradeLosses / numberTrades) * (tradeLossesValue / tradeLosses)))
print("Fixed trade size: ", fixedTradeDollars)
# Sharpe ratio...probably not correct math
#import math
#print(np.mean(tradeGainDollars))
#print(np.std(tradeGainDollars))
#print(math.sqrt(numberTradeDays)*(np.mean(tradeGainDollars)/np.std(tradeGainDollars)))
#### end ####
#### end and save Shadow trades ####
df_to_save = tradesDataFull[['valBeLong', 'gainAhead', 'Close']].copy()
df_to_save.reset_index(level=df_to_save.index.names, inplace=True)
df_to_save.columns = ['Date', 'signal', 'gainAhead', 'Close']
#print(df_to_save)
dSet.save_csv(system_directory, system_name, 'OOS_Equity', 'new',
df_to_save)
dSet.save_csv(system_directory, system_name, 'OOS_Equity', 'dbd',
df_to_save)
# print(df_to_save.tail(10))
#
# print('********************************************************')
# print('\n********************************************************')
# print('\n********************************************************\n\n\n\n')