def call_backtest (params):
# Split params tuple to separate variables
i = params[0]
j = params[1]
strategy = TS() # Instantiate trading dsystem
# Apply srtategy paramters and needed overrides to default TS settings
strategy.settings_ts['markets'] = markets # Overrides initial in TS - optional
strategy.settings_ts['lookback'] = lookback # Overrides initial in TS - optional
strategy.params_ts['periodLong'] = i # Apply one parameter of the trading system
strategy.params_ts['periodShort'] = j # Apply second parameter ....
# Display stdout message
# Messages written out get messed up due to concurrent execution
print "Evaluating for long: %d and short: %d"%(i,j)
# Backtest trading system with parameters
# without showing graphics after the run
results=quantiacsToolbox.runts(strategy,False)
# Send backtest results back - must be combined in a single object - tuple in this case
# Returning back also input parameters to link with "results" of backtesting
# as imap_unoredered was used for multiprocessing and order of returned results is not
# the same as order of parameters
return (i, j, results)
def mySettings():
settings = {}
settings['markets'] = ['AAPL', 'ABT', 'ABBV']
settings['beginInSample'] = '20190101'
settings['endInSample'] = '20200101'
settings['lookback'] = 100
settings['budget'] = 10**6
settings['slippage'] = 0.05
return settings
if __name__ == '__main__':
import quantiacsToolbox
results = quantiacsToolbox.runts(__file__)
def call_backtest(params):
i = params[0]
j = params[1]
strategy = TS() # Instantiate
strategy.settings_ts['markets'] = markets # Overrides initial in TS
strategy.settings_ts['lookback'] = lookback # Overrides initial in TS
strategy.params_ts[
'periodLong'] = i # Apply long period from argument to trading system
strategy.params_ts['periodShort'] = j # Short period
# Display message
print "Evaluating for long: %d and short: %d" % (i, j)
# Backtest trading system defined in "strategy" (TS class object)
# without showing graphics after the run
results = quantiacsToolbox.runts(strategy, False)
# Send backtest results back
return (i, j, results)
def evalSecurities(individual):
#print "strategy: ",individual.__str__()
# Transform the tree expression in a callable function
code = str(individual)
print
print "code: ", code
func = toolbox.compile(expr=individual)
#pdb.set_trace()
# Randomly sample 400 mails in the spam database
#spam_samp = random.sample(spam, 400)
# Evaluate the sum of correctly identified mail as spam
#result = sum(bool(func(*mail[:5])) is bool(mail[57]) for mail in spam_samp)
strat = Strat()
strat.strategy = individual
#pdb.set_trace()
result = quantiacsToolbox.runts(strat, plotEquity=False)
sharpe = result['stats']['sharpe']
if numpy.isnan(sharpe):
sharpe = -10
print "sharpe: ", sharpe
#print result
#pdb.set_trace()
#try:
# print individual[0].seq
#except:
# #pdb.set_trace()
# pass
return sharpe,
pos[0] = 1
else:
pos[0] = -1
return pos, settings
##### Do not change this function definition #####
def mySettings():
''' Define your trading system settings here '''
settings = {}
# Futures Contracts
settings['markets'] = ['F_US']
settings['slippage'] = 0.05
settings['budget'] = 1000000
settings['lookback'] = 252
settings['beginInSample'] = '20140101'
settings['endInSample'] = '20170101'
return settings
# Evaluate trading system defined in current file.
if __name__ == '__main__':
from quantiacsToolbox import runts
np.random.seed(98274534)
results = runts(__file__)
settings['n_sharpe'] = 252 # This many timesteps to compute Sharpes.
settings['horizon'] = settings['n_time'] - settings['n_sharpe'] + 1
settings['num_epochs'] = 100 # Number of epochs each day.
settings['batch_size'] = 32
settings['val_period'] = 16
settings['lr'] = 1e-2 # Learning rate.
settings['iter'] = 0
settings['lookback'] = 1000
settings['budget'] = 10**6
settings['slippage'] = 0.05
settings['beginInSample'] = '20010104'
settings['endInSample'] = '20131231'
settings['retrain_interval'] = 1000
settings['allow_shorting'] = True
settings['lr_mult_base'] = 1.
settings['restart_variables'] = True
settings['nn'] = None
settings['data_types'] = [1, 4]
settings['markets'] = ['AAPL', 'GOOG', 'MMM', 'CASH']
settings['past_data'] = training.make_empty_datadict(settings['markets'])
return settings
if __name__ == '__main__':
import quantiacsToolbox
RESULTS = quantiacsToolbox.runts(__file__)
print RESULTS['stats']
RESULTS['nn'] = None
joblib.dump(RESULTS, 'saved_data/results_of_this_run.pkl')
'F_RR', 'F_RU', 'F_RY', 'F_S', 'F_SB', 'F_SF', 'F_SH', 'F_SI',
'F_SM', 'F_SS', 'F_SX', 'F_TR', 'F_TU', 'F_TY', 'F_UB', 'F_US',
'F_UZ', 'F_VF', 'F_VT', 'F_VW', 'F_VX', 'F_W', 'F_XX', 'F_YM',
'F_ZQ'
]
settings['beginInSample'] = '2011101'
settings['endInSample'] = '20200101'
settings['lookback'] = 200
settings['budget'] = 10**6
settings['slippage'] = 0.05
settings['participation'] = 0.1
settings['days_passed'] = 0
settings['spy_under_short_sma'] = 0
settings['spy_over_short_sma'] = 0
settings['logUrl'] = True
return settings
# Evaluate trading system defined in current file.
if __name__ == '__main__':
import quantiacsToolbox
parser = argparse.ArgumentParser(description='Run strategy backtrace.')
args = parser.parse_args()
results = quantiacsToolbox.runts(__file__)
settings = {}
# Futures Contracts
settings['markets'] = [
'CASH', 'F_AD', 'F_AE', 'F_AH', 'F_AX', 'F_BC', 'F_BG', 'F_BO', 'F_BP',
'F_C', 'F_CA', 'F_CC', 'F_CD', 'F_CF', 'F_CL', 'F_CT', 'F_DL', 'F_DM',
'F_DT', 'F_DX', 'F_DZ', 'F_EB', 'F_EC', 'F_ED', 'F_ES', 'F_F', 'F_FB',
'F_FC', 'F_FL', 'F_FM', 'F_FP', 'F_FV', 'F_FY', 'F_GC', 'F_GD', 'F_GS',
'F_GX', 'F_HG', 'F_HO', 'F_HP', 'F_JY', 'F_KC', 'F_LB', 'F_LC', 'F_LN',
'F_LQ', 'F_LR', 'F_LU', 'F_LX', 'F_MD', 'F_MP', 'F_ND', 'F_NG', 'F_NQ',
'F_NR', 'F_NY', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_PQ', 'F_RB', 'F_RF',
'F_RP', 'F_RR', 'F_RU', 'F_RY', 'F_S', 'F_SB', 'F_SF', 'F_SH', 'F_SI',
'F_SM', 'F_SS', 'F_SX', 'F_TR', 'F_TU', 'F_TY', 'F_UB', 'F_US', 'F_UZ',
'F_VF', 'F_VT', 'F_VW', 'F_VX', 'F_W', 'F_XX', 'F_YM', 'F_ZQ'
]
settings['lookback'] = 252
settings['budget'] = 10**6
settings['slippage'] = 0.05
settings['gap'] = 20
settings['dimension'] = 5
return settings
if __name__ == '__main__':
import quantiacsToolbox
quantiacsToolbox.runts(__file__)
def sim(plan): returnDict = quantiacsToolbox.runts(sys.argv[1], plotEquity=False, plan=plan) return returnDict['stats']['sharpe']
import numpy as np
from scipy.signal import lfilter
def mySettings():
settings = {}
settings['markets'] = ['CASH', 'F_AD', 'F_BO', 'F_BP', 'F_C', 'F_CC', 'F_CD',
'F_CL', 'F_CT', 'F_DX', 'F_EC', 'F_ED', 'F_ES', 'F_FC', 'F_FV', 'F_GC',
'F_HG', 'F_HO', 'F_JY', 'F_KC', 'F_LB', 'F_LC', 'F_LN', 'F_MD', 'F_MP',
'F_NG', 'F_NQ', 'F_NR', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_RB', 'F_RU',
'F_S', 'F_SB', 'F_SF', 'F_SI', 'F_SM', 'F_TU', 'F_TY', 'F_US', 'F_W',
'F_XX', 'F_YM']
settings['slippage'] = 0.05
settings['budget'] = 1000000
settings['beginInSample'] = '20050101'
settings['endInSample'] = '20121231'
settings['lookback'] = 504
return settings
def myTradingSystem(DATE, OPEN, HIGH, LOW, CLOSE, settings):
period1 = 504
rsi1 = RSI(CLOSE, period1)
p = rsi1 - 50
return p, settings
def RSI(CLOSE,period):
closeMom = CLOSE[1:, :] - CLOSE[:-1, :]
upPosition = np.where(closeMom >= 0)
downPosition = np.where(closeMom < 0)
upMoves = closeMom.copy()
upMoves[downPosition] = 0
import quantiacsToolbox
returnDict = quantiacsToolbox.runts("strats/s102.py")
'''
a_new = a_old - lr * grad(F(a_old))
{\displaystyle \mathbf {a} _{n+1}=\mathbf {a} _{n}-\gamma \nabla F(\mathbf {a} _{n})}
'''
i = 0
j_org = 2 * w_1 + 4 * (w_2 * w_2 * w_2)
while (True):
w_1_new = w_1 - lr * (2 * w_1)
w_2_new = w_2 - lr * (w_2 * w_2 * w_2)
delta = (w_1_new - w_1) * (w_1_new - w_1) + (w_2_new -
w_2) * (w_2_new - w_2)
delta = math.sqrt(delta)
i += 1
print('iteration: ' + str(i) + ' delta: ' + str(delta))
if delta < success:
break
if i > 1000000:
break
w_1 = w_1_new
w_2 = w_2_new
sys.exit(0)
from quantiacsToolbox import runts
runts()
return overall_pos, settings
def mySettings(self):
return settings
if __name__ == '__main__':
settings = mainSettings()
nMarkets = len(settings['markets'])
macd_indicator = np.zeros(nMarkets)
rsi_indicator = np.zeros(nMarkets)
bb_indicator = np.zeros(nMarkets)
res_MACD = quantiacsToolbox.runts(MACD_strat, plotEquity = False)
mktEquity_MACD = np.array(res_MACD['marketEquity'])
res_RSI = quantiacsToolbox.runts(RSI_strat, plotEquity = False)
mktEquity_RSI = np.array(res_RSI['marketEquity'])
res_BB = quantiacsToolbox.runts(BB_strat, plotEquity = False)
mktEquity_BB = np.array(res_BB['marketEquity'])
for i in range(1, nMarkets):
try:
stat_MACD = quantiacsToolbox.stats(mktEquity_MACD[-56:, i])
if stat_MACD['sharpe'] > 0:
macd_indicator[i] = 1
stat_RSI = quantiacsToolbox.stats(mktEquity_RSI[-56:, i])
import numpy as np
Long = np.transpose(mktExposure)
Long[Long < 0] = 0
Long = Long[:-1] # Market Exposure lagged by one day
Long = Long.sum(axis=0)
return Long
def CalcShortExposure(mktExposure, lookBack):
import numpy as np
Short = -np.transpose(mktExposure)
Short[Short < 0] = 0
Short = Short[:-1] # Market Exposure lagged by one day
Short = Short.sum(axis=0)
return Short
# Evaluate trading system defined in current file.
if __name__ == '__main__':
from quantiacsToolbox import runts, optimize
#optimize()
res = runts(reloadData=False)
l1 = CalcLongExposure(res['marketExposure'], 0)
s1 = CalcShortExposure(res['marketExposure'], 0)
sys.exit(0)
closeRange = np.ptp(CLOSE[-4:,1])
atr = ATR(HIGH, LOW, CLOSE, 4)
LongRule1 = CLOSE[-1,1] < CLOSE[-2,1] and closeRange < atr[1]
ShortRule1 = CLOSE[-1,1] > CLOSE[-2,1] and closeRange < atr[1]
if LongRule1:
p[0], p[1] = 0, 1
if ShortRule1:
p[0], p[1] = 0, -1
return p, settings
def mySettings():
settings = {}
settings['markets'] = ['CASH','F_CL']
settings['slippage'] = 0.0
settings['budget'] = 1000000
settings['beginInSample'] = '20040101'
settings['endInSample'] = '20140101'
settings['lookback'] = 504
return settings
if __name__=='__main__':
import quantiacsToolbox
results = quantiacsToolbox.runts(__file__)
longEquity = smaRecent > smaLong
shortEquity = ~longEquity
pos = numpy.zeros((1, nMarkets))
pos[0, longEquity] = 1
pos[0, shortEquity] = -1
weights = pos / numpy.nansum(abs(pos))
return weights, settings
def mySettings(self):
"""
Return settings dictionary
"""
return self.settings_ts
# For testing this strategy as standalone, and when testing best parameters
# resulted from optimisation
if __name__ == '__main__':
import quantiacsToolbox
# Instantiate trading system
strategy = TS()
# Run backtest
results = quantiacsToolbox.runts(strategy)
def mySettings():
""" Define your trading system settings here """
settings = {}
# Futures Contracts
settings['markets'] = ['CASH', 'F_AD', 'F_AE', 'F_AH', 'F_AX', 'F_BC', 'F_BG', 'F_BO', 'F_BP', 'F_C', 'F_CA',
'F_CC', 'F_CD', 'F_CF', 'F_CL', 'F_CT', 'F_DL', 'F_DM', 'F_DT', 'F_DX', 'F_DZ', 'F_EB',
'F_EC', 'F_ED', 'F_ES', 'F_F', 'F_FB', 'F_FC', 'F_FL', 'F_FM', 'F_FP', 'F_FV', 'F_FY',
'F_GC', 'F_GD', 'F_GS', 'F_GX', 'F_HG', 'F_HO', 'F_HP', 'F_JY', 'F_KC', 'F_LB', 'F_LC',
'F_LN', 'F_LQ', 'F_LR', 'F_LU', 'F_LX', 'F_MD', 'F_MP', 'F_ND', 'F_NG', 'F_NQ', 'F_NR',
'F_NY', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_PQ', 'F_RB', 'F_RF', 'F_RP', 'F_RR', 'F_RU',
'F_RY', 'F_S', 'F_SB', 'F_SF', 'F_SH', 'F_SI', 'F_SM', 'F_SS', 'F_SX', 'F_TR', 'F_TU',
'F_TY', 'F_UB', 'F_US', 'F_UZ', 'F_VF', 'F_VT', 'F_VW', 'F_VX', 'F_W', 'F_XX', 'F_YM',
'F_ZQ']
settings['lookback'] = 252
settings['budget'] = 10 ** 6
settings['slippage'] = 0.05
settings['gap'] = 20
settings['dimension'] = 5
return settings
if __name__ == '__main__':
import quantiacsToolbox
quantiacsToolbox.runts(__file__)
import numpy as np
from scipy.signal import lfilter
def mySettings():
settings = {}
settings['markets'] = ['CASH', 'AAPL', 'AMZN', 'GOOGL', 'GS', 'JPM', 'MS', \
'MSFT', 'T']
#settings['markets'] = ['CASH', 'F_AD', 'F_BO', 'F_BP', 'F_C', 'F_CC', 'F_CD', 'F_CL', 'F_CT', 'F_DX', 'F_EC', 'F_ED', 'F_ES', 'F_FC', 'F_FV', 'F_GC', 'F_HG', 'F_HO', 'F_JY', 'F_KC', 'F_LB', 'F_LC', 'F_LN', 'F_MD', 'F_MP', 'F_NG', 'F_NQ', 'F_NR', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_RB', 'F_RU', 'F_S', 'F_SB', 'F_SF', 'F_SI', 'F_SM', 'F_TU', 'F_TY', 'F_US', 'F_W', 'F_XX', 'F_YM']
settings['slippage'] = 0.15
settings['budget'] = 3000000
settings['beginInSample'] = '20150102'
settings['endInSample'] = '20170531'
settings['lookback'] = 504
return settings
def myTradingSystem(DATE, OPEN, HIGH, LOW, CLOSE, settings):
period1 = 504
rsi1 = RSI(CLOSE, period1)
p = rsi1 - 50
return p, settings
def RSI(CLOSE, period):
closeMom = CLOSE[1:, :] - CLOSE[:-1, :]
upPosition = np.where(closeMom >= 0)
downPosition = np.where(closeMom < 0)
upMoves = closeMom.copy()
upMoves[downPosition] = 0
#print upMoves[upPosition] + "\n"
downMoves = np.abs(closeMom.copy())
downMoves[upPosition] = 0
def mySettings():
settings['markets'] = [
'CASH', 'AAPL', 'ABBV', 'ABT', 'ACN', 'AEP', 'AIG', 'ALL', 'AMGN',
'AMZN', 'APA', 'APC', 'AXP', 'BA', 'BAC', 'BAX', 'BK', 'BMY', 'BRKB',
'C', 'CAT', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CSCO', 'CVS', 'CVX',
'DD', 'DIS', 'DOW', 'DVN', 'EBAY', 'EMC', 'EMR', 'EXC', 'F', 'FB',
'FCX', 'FDX', 'FOXA', 'GD', 'GE', 'GILD', 'GM', 'GOOGL', 'GS', 'HAL',
'HD', 'HON', 'HPQ', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'LLY', 'LMT',
'LOW', 'MA', 'MCD', 'MDLZ', 'MDT', 'MET', 'MMM', 'MO', 'MON', 'MRK',
'MS', 'MSFT', 'NKE', 'NOV', 'NSC', 'ORCL', 'OXY', 'PEP', 'PFE', 'PG',
'PM', 'QCOM', 'RTN', 'SBUX', 'SLB', 'SO', 'SPG', 'T', 'TGT', 'TWX',
'TXN', 'UNH', 'UNP', 'UPS', 'USB', 'UTX', 'V', 'VZ', 'WAG', 'WFC',
'WMT', 'XOM'
]
settings['beginInSample'] = '20170101'
settings['endInSample'] = '20200101'
settings['lookback'] = 300
settings['budget'] = 10**6
settings['slippage'] = 0.05
return settings
if __name__ == "__main__":
import quantiacsToolbox
results = quantiacsToolbox.runts(
'/Users/rostammahabadi/Documents/Projects/TrendfollowingQuantiacs.py')
import quantiacsToolbox
returnDict = quantiacsToolbox.runts('strats/s100.py')
# 'DVN','EBAY','EMC','EMR','EXC','F','FB','FCX','FDX','FOXA','GD','GE',
# 'GILD','GM','GOOGL','GS','HAL','HD','HON','HPQ','IBM','INTC','JNJ','JPM',
# 'KO','LLY','LMT','LOW','MA','MCD','MDLZ','MDT','MET','MMM','MO','MON',
# 'MRK','MS','MSFT','NKE','NOV','NSC','ORCL','OXY','PEP','PFE','PG','PM',
# 'QCOM','RTN','SBUX','SLB','SO','SPG','T','TGT','TWX','TXN','UNH','UNP',
# 'UPS','USB','UTX','V','VZ','WAG','WFC','WMT','XOM']
# Futures Contracts
settings['markets'] = [
'CASH', 'F_AD', 'F_BO', 'F_BP', 'F_C', 'F_CC', 'F_CD', 'F_CL', 'F_CT',
'F_DX', 'F_EC', 'F_ED', 'F_ES', 'F_FC', 'F_FV', 'F_GC', 'F_HG', 'F_HO',
'F_JY', 'F_KC', 'F_LB', 'F_LC', 'F_LN', 'F_MD', 'F_MP', 'F_NG', 'F_NQ',
'F_NR', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_RB', 'F_RU', 'F_S', 'F_SB',
'F_SF', 'F_SI', 'F_SM', 'F_TU', 'F_TY', 'F_US', 'F_W', 'F_XX', 'F_YM'
]
settings['beginInSample'] = '20120506' #start date
settings['endInSample'] = '20190410' #end date
settings['lookback'] = 504 #how many data points prior to last n days
settings['budget'] = 10**6 #$1M budget
settings[
'slippage'] = 0.05 #brokerage commisions, market slippage, inflation, etc.
return settings
# Evaluate trading system defined in current file.
if __name__ == '__main__':
import quantiacsToolbox
results = quantiacsToolbox.runts('trendFollowing.py')
import numpy as np
from scipy.signal import lfilter
def mySettings():
settings={}
settings['markets'] = ['CASH', 'F_AD', 'F_BO', 'F_BP', 'F_C', 'F_CC', 'F_CD', 'F_CL', 'F_CT', 'F_DX', 'F_EC', 'F_ED', 'F_ES', 'F_FC', 'F_FV', 'F_GC', 'F_HG', 'F_HO', 'F_JY', 'F_KC', 'F_LB', 'F_LC', 'F_LN', 'F_MD', 'F_MP', 'F_NG', 'F_NQ', 'F_NR', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_RB', 'F_RU', 'F_S', 'F_SB', 'F_SF', 'F_SI', 'F_SM', 'F_TU', 'F_TY', 'F_US', 'F_W', 'F_XX', 'F_YM']
settings['slippage'] = 0.05
settings['budget'] = 1000000
# settings['beginInSample'] = '20050101'
settings['endInSample'] = '20121231'
settings['lookback'] = 504
return settings
def myTradingSystem(DATE, OPEN, HIGH, LOW, CLOSE, settings):
period1 = 504
rsi1 = RSI(CLOSE,period1)
p = rsi1 -50
return p, settings
def RSI(CLOSE,period):
closeMom = CLOSE[1:,:] - CLOSE[:-1,:]
upPosition = np.where(closeMom >= 0)
downPosition = np.where(closeMom < 0)
upMoves = closeMom.copy()
upMoves[downPosition] = 0
downMoves = np.abs(closeMom.copy())
downMoves[upPosition] = 0
out = 100 - 100 / (1 + (np.mean(upMoves[-(period+1):,:],axis=0) / np.mean(downMoves[-(period+1):,:],axis=0)))
return out
def nDayEMA(field, period):
# 'AMGN','AMZN','APA','APC','AXP','BA','BAC','BAX','BK','BMY','BRKB','C',
# 'CAT','CL','CMCSA','COF','COP','COST','CSCO','CVS','CVX','DD','DIS','DOW',
# 'DVN','EBAY','EMC','EMR','EXC','F','FB','FCX','FDX','FOXA','GD','GE',
# 'GILD','GM','GOOGL','GS','HAL','HD','HON','HPQ','IBM','INTC','JNJ','JPM',
# 'KO','LLY','LMT','LOW','MA','MCD','MDLZ','MDT','MET','MMM','MO','MON',
# 'MRK','MS','MSFT','NKE','NOV','NSC','ORCL','OXY','PEP','PFE','PG','PM',
# 'QCOM','RTN','SBUX','SLB','SO','SPG','T','TGT','TWX','TXN','UNH','UNP',
# 'UPS','USB','UTX','V','VZ','WAG','WFC','WMT','XOM']
# Futures Contracts
settings['markets'] = ['CASH', 'F_AD', 'F_AE', 'F_AH', 'F_AX', 'F_BC', 'F_BG', 'F_BO', 'F_BP', 'F_C', 'F_CA',
'F_CC', 'F_CD', 'F_CF', 'F_CL', 'F_CT', 'F_DL', 'F_DM', 'F_DT', 'F_DX', 'F_DZ', 'F_EB',
'F_EC', 'F_ED', 'F_ES', 'F_F', 'F_FB', 'F_FC', 'F_FL', 'F_FM', 'F_FP', 'F_FV', 'F_FY',
'F_GC', 'F_GD', 'F_GS', 'F_GX', 'F_HG', 'F_HO', 'F_HP', 'F_JY', 'F_KC', 'F_LB', 'F_LC',
'F_LN', 'F_LQ', 'F_LR', 'F_LU', 'F_LX', 'F_MD', 'F_MP', 'F_ND', 'F_NG', 'F_NQ', 'F_NR',
'F_NY', 'F_O', 'F_OJ', 'F_PA', 'F_PL', 'F_PQ', 'F_RB', 'F_RF', 'F_RP', 'F_RR', 'F_RU',
'F_RY', 'F_S', 'F_SB', 'F_SF', 'F_SH', 'F_SI', 'F_SM', 'F_SS', 'F_SX', 'F_TR', 'F_TU',
'F_TY', 'F_UB', 'F_US', 'F_UZ', 'F_VF', 'F_VT', 'F_VW', 'F_VX', 'F_W', 'F_XX', 'F_YM',
'F_ZQ']
settings['lookback'] = 504
settings['budget'] = 10**6
settings['slippage'] = 0.05
return settings
# Evaluate trading system defined in current file.
if __name__ == '__main__':
from quantiacsToolbox import runts
results = runts(__file__)
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 28 17:56:04 2015
@author: yuy
"""
import quantiacsToolbox
returnDict = quantiacsToolbox.runts('H:\training materials\QuantiacsPythonToolbox_v222\pythonToolbox\sampleTradingSystems\meanReversion.py')
quantiacsToolbox.runts
returnDict['tsName'] #a string that contains the name of your trading system
returnDict['fundDate'] #a list of the dates available to your trading system
returnDict['fundTradeDates'] #a list of the dates traded by your trading system
returnDict['fundEquity'] #a list defining the equity of your portfolio
returnDict['marketEquity'] #a list containing the equity made from each market in settings[‘markets’]
returnDict['marketExposure'] #a list containing the allocation of capital to each market in settings[‘markets’]
returnDict['errorLog'] #a list describing any errors made in evaluation
returnDict['runtime'] #a float containing the time required to evaluate your system.
returnDict['evalDate'] #an int that describes the last date of evaluation for your system (in YYYYMMDD format)
returnDict['stats'] #a dict containing the performance statistics of your system
returnDict['settings'] #a dict containting the settings defined in mySettings
