def dual_thrust(asset, start_date, end_date, scenarios, config):
nearby = config["nearby"]
rollrule = config["rollrule"]
start_d = misc.day_shift(start_date, "-2b")
file_prefix = config["file_prefix"] + "_" + asset + "_"
mdf = misc.nearby(asset, nearby, start_d, end_date, rollrule, "m", need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
output = {}
for ix, s in enumerate(scenarios):
config["win"] = s[1]
config["k"] = s[0]
config["m"] = s[2]
config["f"] = s[3]
(res, closed_trades, ts) = dual_thrust_sim(mdf, config)
output[ix] = res
print "saving results for scen = %s" % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + "_trades.csv"
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + "_dailydata.csv"
ts.to_csv(fname)
fname = file_prefix + "stats.csv"
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def dual_thrust( asset, start_date, end_date, scenarios, config):
nearby = config['nearby']
rollrule = config['rollrule']
start_d = misc.day_shift(start_date, '-4b')
file_prefix = config['file_prefix'] + '_' + asset + '_'
ddf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'm', need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, s in enumerate(scenarios):
config['win'] = s[1]
config['k'] = s[0]
config['m'] = s[2]
(res, closed_trades, ts) = dual_thrust_sim( ddf, mdf, config)
output[ix] = res
print 'saving results for scen = %s' % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def dual_thrust( asset, start_date, end_date, scenarios, config):
nearby = config['nearby']
rollrule = config['rollrule']
start_d = misc.day_shift(start_date, '-2b')
file_prefix = config['file_prefix'] + '_' + asset + '_'
ddf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'm', need_shift=True)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, s in enumerate(scenarios):
config['win'] = s[1]
config['k'] = s[0]
config['m'] = s[2]
(res, closed_trades, ts) = dual_thrust_sim( ddf, mdf, config)
output[ix] = res
print 'saving results for scen = %s' % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def fisher_swing(asset, start_date, end_date, freqs, windows, config):
nearby = config["nearby"]
rollrule = config["rollrule"]
file_prefix = config["file_prefix"] + "_" + asset + "_"
df = misc.nearby(asset, nearby, start_date, end_date, rollrule, "m", need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix * 10 + iy
config["win"] = win
config["freq"] = freq
(res, closed_trades, ts) = fisher_swing_sim(df, xdf, config)
output[idx] = res
print "saving results for scen = %s" % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + "_trades.csv"
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + "_dailydata.csv"
ts.to_csv(fname)
fname = file_prefix + "stats.csv"
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def r_breaker( asset, start_date, end_date, scenarios, freqs, config):
nearby = config['nearby']
rollrule = config['rollrule']
start_d = misc.day_shift(start_date, '-1b')
file_prefix = config['file_prefix'] + '_' + asset + '_'
ddf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, freq in enumerate(freqs):
if freq !='1min':
df = dh.conv_ohlc_freq(mdf, freq)
else:
df = mdf
for iy, k in enumerate(scenarios):
idx = ix*10+iy
config['k'] = k
(res, closed_trades, ts) = r_breaker_sim( ddf, df, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def aberration( asset, start_date, end_date, freqs, windows, config):
nearby = config['nearby']
rollrule = config['rollrule']
file_prefix = config['file_prefix'] + '_' + asset + '_'
df = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix*10+iy
config['win'] = win
(res, closed_trades, ts) = aberration_sim( xdf, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def fisher_swing(asset, start_date, end_date, freqs, windows, config):
nearby = config['nearby']
rollrule = config['rollrule']
file_prefix = config['file_prefix'] + '_' + asset + '_'
df = misc.nearby(asset,
nearby,
start_date,
end_date,
rollrule,
'm',
need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix * 10 + iy
config['win'] = win
config['freq'] = freq
(res, closed_trades, ts) = fisher_swing_sim(df, xdf, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def turtle( asset, start_date, end_date, systems, config):
rollrule = config['rollrule']
nearby = config['nearby']
file_prefix = config['file_prefix'] + '_' + asset + '_'
start_d = misc.day_shift(start_date, '-'+str(max([ max(sys) for sys in systems]))+'b')
ddf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, sys in enumerate(systems):
config['signals'] = sys[:3]
config['max_loss'] = sys[3]
config['max_pos'] = sys[4]
(res, closed_trades, ts) = turtle_sim( ddf, mdf, config)
output[ix] = res
print 'saving results for scen = %s' % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def simlauncher_min(config_file):
sim_config = {}
with open(config_file, 'r') as fp:
sim_config = json.load(fp)
bktest_split = sim_config['sim_func'].split('.')
bktest_module = __import__(bktest_split[0])
run_sim = getattr(bktest_module, bktest_split[1])
dir_name = config_file.split('.')[0]
test_folder = get_bktest_folder()
file_prefix = test_folder + dir_name + os.path.sep
if not os.path.exists(file_prefix):
os.makedirs(file_prefix)
sim_list = sim_config['products']
config = {}
start_date = datetime.datetime.strptime(sim_config['start_date'],
'%Y%m%d').date()
config['start_date'] = start_date
end_date = datetime.datetime.strptime(sim_config['end_date'],
'%Y%m%d').date()
config['end_date'] = end_date
scen_dim = [len(sim_config[s]) for s in sim_config['scen_keys']]
outcol_list = ['asset', 'scenario'] + sim_config['scen_keys'] \
+ ['sharp_ratio', 'tot_pnl', 'std_pnl', 'num_days', \
'max_drawdown', 'max_dd_period', 'profit_dd_ratio', \
'all_profit', 'tot_cost', 'win_ratio', 'num_win', 'num_loss', \
'profit_per_win', 'profit_per_loss']
scenarios = [list(s) for s in np.ndindex(tuple(scen_dim))]
config.update(sim_config['config'])
config['pos_class'] = eval(sim_config['pos_class'])
if 'proc_func' in sim_config:
config['proc_func'] = eval(sim_config['proc_func'])
file_prefix = file_prefix + sim_config['sim_name']
if config['close_daily']:
file_prefix = file_prefix + 'daily_'
config['file_prefix'] = file_prefix
summary_df = pd.DataFrame()
fname = config['file_prefix'] + 'summary.csv'
if os.path.isfile(fname):
summary_df = pd.DataFrame.from_csv(fname)
for asset in sim_list:
file_prefix = config['file_prefix'] + '_' + asset + '_'
fname = file_prefix + 'stats.json'
output = {}
if os.path.isfile(fname):
with open(fname, 'r') as fp:
output = json.load(fp)
if len(output.keys()) < len(scenarios):
if asset in sim_start_dict:
start_date = max(sim_start_dict[asset], config['start_date'])
else:
start_date = config['start_date']
if 'offset' in sim_config:
config[
'offset'] = sim_config['offset'] * trade_offset_dict[asset]
else:
config['offset'] = trade_offset_dict[asset]
config['marginrate'] = (sim_margin_dict[asset],
sim_margin_dict[asset])
config['nearby'] = 1
config['rollrule'] = '-50b'
config['exit_min'] = 2112
config['no_trade_set'] = range(300, 301) + range(
1500, 1501) + range(2059, 2100)
if asset in ['cu', 'al', 'zn']:
config['nearby'] = 3
config['rollrule'] = '-1b'
elif asset in ['IF', 'IH', 'IC']:
config['rollrule'] = '-2b'
config['no_trade_set'] = range(1515, 1520) + range(2110, 2115)
elif asset in ['au', 'ag']:
config['rollrule'] = '-25b'
elif asset in ['TF', 'T']:
config['rollrule'] = '-20b'
config['no_trade_set'] = range(1515, 1520) + range(2110, 2115)
config['no_trade_set'] = []
nearby = config['nearby']
rollrule = config['rollrule']
if nearby > 0:
mdf = misc.nearby(asset,
nearby,
start_date,
end_date,
rollrule,
'm',
need_shift=True,
database='hist_data')
mdf = cleanup_mindata(mdf, asset)
if 'need_daily' in sim_config:
ddf = misc.nearby(asset,
nearby,
start_date,
end_date,
rollrule,
'd',
need_shift=True,
database='hist_data')
config['ddf'] = ddf
for ix, s in enumerate(scenarios):
fname1 = file_prefix + str(ix) + '_trades.csv'
fname2 = file_prefix + str(ix) + '_dailydata.csv'
if os.path.isfile(fname1) and os.path.isfile(fname2):
continue
for key, seq in zip(sim_config['scen_keys'], s):
config[key] = sim_config[key][seq]
df = mdf.copy(deep=True)
(res, closed_trades, ts) = run_sim(df, config)
res.update(dict(zip(sim_config['scen_keys'], s)))
res['asset'] = asset
output[ix] = res
print 'saving results for asset = %s, scen = %s' % (asset,
str(ix))
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + 'stats.json'
try:
with open(fname, 'w') as ofile:
json.dump(output, ofile)
except:
continue
res = pd.DataFrame.from_dict(output, orient='index')
res.index.name = 'scenario'
res = res.sort(columns=['sharp_ratio'], ascending=False)
res = res.reset_index()
res.set_index(['asset', 'scenario'])
out_res = res[outcol_list]
if len(summary_df) == 0:
summary_df = out_res[:10].copy(deep=True)
else:
summary_df = summary_df.append(out_res[:10])
fname = config['file_prefix'] + 'summary.csv'
summary_df.to_csv(fname)
return
def simcontract_min(config_file):
sim_config = {}
with open(config_file, 'r') as fp:
sim_config = json.load(fp)
bktest_split = sim_config['sim_func'].split('.')
run_sim = __import__('.'.join(bktest_split[:-1]))
for i in range(1, len(bktest_split)):
run_sim = getattr(run_sim, bktest_split[i])
dir_name = config_file.split('.')[0]
dir_name = dir_name.split(os.path.sep)[-1]
test_folder = get_bktest_folder()
file_prefix = test_folder + dir_name + os.path.sep
if not os.path.exists(file_prefix):
os.makedirs(file_prefix)
sim_list = sim_config['products']
if type(sim_list[0]).__name__ != 'list':
sim_list = [[str(asset)] for asset in sim_list]
sim_mode = sim_config.get('sim_mode', 'OR')
calc_coeffs = sim_config.get('calc_coeffs', [1, -1])
cont_maplist = sim_config.get('cont_maplist', [])
sim_period = sim_config.get('sim_period', '-12m')
need_daily = sim_config.get('need_daily', False)
if len(cont_maplist) == 0:
cont_maplist = [[0]] * len(sim_list)
config = {}
start_date = datetime.datetime.strptime(sim_config['start_date'], '%Y%m%d').date()
config['start_date'] = start_date
end_date = datetime.datetime.strptime(sim_config['end_date'], '%Y%m%d').date()
config['end_date'] = end_date
scen_dim = [ len(sim_config[s]) for s in sim_config['scen_keys']]
outcol_list = ['asset', 'scenario'] + sim_config['scen_keys'] \
+ ['sharp_ratio', 'tot_pnl', 'std_pnl', 'num_days', \
'max_drawdown', 'max_dd_period', 'profit_dd_ratio', \
'all_profit', 'tot_cost', 'win_ratio', 'num_win', 'num_loss', \
'profit_per_win', 'profit_per_loss']
scenarios = [list(s) for s in np.ndindex(tuple(scen_dim))]
config.update(sim_config['config'])
if 'pos_class' in sim_config:
config['pos_class'] = eval(sim_config['pos_class'])
if 'proc_func' in sim_config:
config['proc_func'] = eval(sim_config['proc_func'])
file_prefix = file_prefix + sim_config['sim_name']
if 'close_daily' in config and config['close_daily']:
file_prefix = file_prefix + 'daily_'
config['file_prefix'] = file_prefix
summary_df = pd.DataFrame()
fname = config['file_prefix'] + 'summary.csv'
if os.path.isfile(fname):
summary_df = pd.DataFrame.from_csv(fname)
for assets, cont_map in zip(sim_list, cont_maplist):
file_prefix = config['file_prefix'] + '_' + sim_mode + '_' + '_'.join(assets) + '_'
fname = file_prefix + 'stats.json'
output = {'total': {}, 'cont': {}}
if os.path.isfile(fname):
with open(fname, 'r') as fp:
output = json.load(fp)
#if len(output['total'].keys()) == len(scenarios):
# continue
min_data = {}
day_data = {}
config['tick_base'] = 0
config['marginrate'] = (0, 0)
rollrule = '-50b'
config['exit_min'] = config.get('exit_min', 2057)
config['no_trade_set'] = config.get('no_trade_set', [])
if assets[0] in ['cu', 'al', 'zn']:
rollrule = '-1b'
elif assets[0] in ['IF', 'IH', 'IC']:
rollrule = '-2b'
elif assets[0] in ['au', 'ag']:
rollrule = '-25b'
elif assets[0] in ['TF', 'T']:
rollrule = '-20b'
rollrule = config.get('rollrule', rollrule)
contlist = {}
exp_dates = {}
for i, prod in enumerate(assets):
cont_mth, exch = mysqlaccess.prod_main_cont_exch(prod)
contlist[prod] = misc.contract_range(prod, exch, cont_mth, start_date, end_date)
exp_dates[prod] = [misc.contract_expiry(cont) for cont in contlist[prod]]
edates = [ misc.day_shift(d, rollrule) for d in exp_dates[prod] ]
sdates = [ misc.day_shift(d, sim_period) for d in exp_dates[prod] ]
config['tick_base'] += trade_offset_dict[prod]
config['marginrate'] = ( max(config['marginrate'][0], sim_margin_dict[prod]), max(config['marginrate'][1], sim_margin_dict[prod]))
min_data[prod] = {}
day_data[prod] = {}
for cont, sd, ed in zip(contlist[prod], sdates, edates):
minid_start = 1500
minid_end = 2114
if prod in misc.night_session_markets:
minid_start = 300
tmp_df = mysqlaccess.load_min_data_to_df('fut_min', cont, sd, ed, minid_start, minid_end, database = 'hist_data')
tmp_df['contract'] = cont
min_data[prod][cont] = cleanup_mindata( tmp_df, prod)
if need_daily:
tmp_df = mysqlaccess.load_daily_data_to_df('fut_daily', cont, sd, ed, database = 'hist_data')
day_data[prod][cont] = tmp_df
if 'offset' in sim_config:
config['offset'] = sim_config['offset'] * config['tick_base']
else:
config['offset'] = config['tick_base']
for ix, s in enumerate(scenarios):
fname1 = file_prefix + str(ix) + '_trades.csv'
fname2 = file_prefix + str(ix) + '_dailydata.csv'
if os.path.isfile(fname1) and os.path.isfile(fname2):
continue
for key, seq in zip(sim_config['scen_keys'], s):
config[key] = sim_config[key][seq]
df_list = []
trade_list = []
for idx in range(abs(min(cont_map)), len(contlist[assets[0]]) - max(cont_map)):
cont = contlist[assets[0]][idx]
edate = misc.day_shift(exp_dates[assets[0]][idx], rollrule)
if sim_mode == 'OR':
mdf = min_data[assets[0]][cont]
mdf = mdf[mdf.date <= edate]
if need_daily:
ddf = day_data[assets[0]][cont]
config['ddf'] = ddf[ddf.index <= edate]
if len(config['ddf']) < 10:
continue
else:
mode_keylist = sim_mode.split('-')
smode = mode_keylist[0]
cmode = mode_keylist[1]
all_data = []
if smode == 'TS':
all_data = [min_data[assets[0]][contlist[assets[0]][idx+i]] for i in cont_map]
else:
all_data = [min_data[asset][contlist[asset][idx+i]] for asset, i in zip(assets, cont_map)]
if cmode == 'Full':
mdf = pd.concat(all_data, axis = 1, join = 'inner')
mdf.columns = [iter + str(i) for i, x in enumerate(all_data) for iter in x.columns]
mdf = mdf[ mdf.date0 < edate]
else:
#print all_data[0], all_data[1]
for i, (coeff, tmpdf) in enumerate(zip(calc_coeffs, all_data)):
if i == 0:
xopen = tmpdf['open'] * coeff
xclose = tmpdf['close'] * coeff
else:
xopen = xopen + tmpdf['open'] * coeff
xclose = xclose + tmpdf['close'] * coeff
xopen = xopen.dropna()
xclose = xclose.dropna()
xhigh = pd.concat([xopen, xclose], axis = 1).max(axis = 1)
xlow = pd.concat([xopen, xclose], axis = 1).min(axis = 1)
col_list = ['date', 'min_id', 'volume', 'openInterest']
mdf = pd.concat([ xopen, xhigh, xlow, xclose] + [all_data[0][col] for col in col_list], axis = 1, join = 'inner')
mdf.columns = ['open', 'high', 'low', 'close'] + col_list
mdf['contract'] = cont
#print mdf
if need_daily:
if smode == 'TS':
all_data = [day_data[assets[0]][contlist[assets[0]][idx+i]] for i in cont_map]
else:
all_data = [day_data[asset][contlist[asset]][idx+i] for asset, i in zip(assets, cont_map)]
if cmode == 'Full':
ddf = pd.concat(all_data, axis = 1, join = 'inner')
ddf.columns = [iter + str(i) for i, x in enumerate(all_data) for iter in x.columns]
config['ddf'] = ddf[ddf.index <= edate]
else:
for i, (coeff, tmpdf) in enumerate(zip(calc_coeffs, all_data)):
if i == 0:
xopen = tmpdf['open'] * coeff
xclose = tmpdf['close'] * coeff
else:
xopen = xopen + tmpdf['open'] * coeff
xclose = xclose + tmpdf['close'] * coeff
xhigh = pd.concat([xopen, xclose], axis = 1).max(axis = 1)
xlow = pd.concat([xopen, xclose], axis = 1).min(axis = 1)
col_list = ['volume', 'openInterest']
ddf = pd.concat([ xopen, xhigh, xlow, xclose] + [all_data[0][col] for col in col_list], axis = 1, join = 'inner')
ddf.columns = ['open', 'high', 'low', 'close'] + col_list
ddf['contract'] = cont
config['ddf'] = ddf[ddf.index <= edate]
if len(config['ddf']) < 10:
continue
df = mdf.copy(deep = True)
df, closed_trades = run_sim( df, config)
df_list.append(df)
trade_list = trade_list + closed_trades
(res_pnl, ts) = get_pnl_stats( [df], config['capital'], config['marginrate'], 'm')
res_trade = get_trade_stats( trade_list )
res = dict( res_pnl.items() + res_trade.items())
res.update(dict(zip(sim_config['scen_keys'], s)))
res['asset'] = cont
if cont not in output['cont']:
output['cont'][cont] = {}
output['cont'][cont][ix] = res
(res_pnl, ts) = get_pnl_stats( df_list, config['capital'], config['marginrate'], 'm')
res_trade = get_trade_stats( trade_list )
res = dict( res_pnl.items() + res_trade.items())
res.update(dict(zip(sim_config['scen_keys'], s)))
res['asset'] = '_'.join(assets)
output['total'][ix] = res
print 'saving results for asset = %s, scen = %s' % ('_'.join(assets), str(ix))
all_trades = {}
for i, tradepos in enumerate(trade_list):
all_trades[i] = strat.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + 'stats.json'
try:
with open(fname, 'w') as ofile:
json.dump(output, ofile)
except:
continue
cont_df = pd.DataFrame()
for idx in range(abs(min(cont_map)), len(contlist[assets[0]]) - max(cont_map)):
cont = contlist[assets[0]][idx]
if cont not in output['cont']:
continue
res = scen_dict_to_df(output['cont'][cont])
out_res = res[outcol_list]
if len(cont_df) == 0:
cont_df = out_res[:20].copy(deep = True)
else:
cont_df = cont_df.append(out_res[:20])
fname = file_prefix + 'cont_stat.csv'
cont_df.to_csv(fname)
res = scen_dict_to_df(output['total'])
out_res = res[outcol_list]
if len(summary_df) == 0:
summary_df = out_res[:20].copy(deep = True)
else:
summary_df = summary_df.append(out_res[:20])
fname = config['file_prefix'] + 'summary.csv'
summary_df.to_csv(fname)
return
def simlauncher_min(config_file):
sim_config = {}
with open(config_file, 'r') as fp:
sim_config = json.load(fp)
bktest_split = sim_config['sim_func'].split('.')
bktest_module = __import__(bktest_split[0])
run_sim = getattr(bktest_module, bktest_split[1])
dir_name = config_file.split('.')[0]
test_folder = get_bktest_folder()
file_prefix = test_folder + dir_name + os.path.sep
if not os.path.exists(file_prefix):
os.makedirs(file_prefix)
sim_list = sim_config['products']
config = {}
start_date = datetime.datetime.strptime(sim_config['start_date'], '%Y%m%d').date()
config['start_date'] = start_date
end_date = datetime.datetime.strptime(sim_config['end_date'], '%Y%m%d').date()
config['end_date'] = end_date
scen_dim = [ len(sim_config[s]) for s in sim_config['scen_keys']]
outcol_list = ['asset', 'scenario'] + sim_config['scen_keys'] \
+ ['sharp_ratio', 'tot_pnl', 'std_pnl', 'num_days', \
'max_drawdown', 'max_dd_period', 'profit_dd_ratio', \
'all_profit', 'tot_cost', 'win_ratio', 'num_win', 'num_loss', \
'profit_per_win', 'profit_per_loss']
scenarios = [list(s) for s in np.ndindex(tuple(scen_dim))]
config.update(sim_config['config'])
config['pos_class'] = eval(sim_config['pos_class'])
if 'proc_func' in sim_config:
config['proc_func'] = eval(sim_config['proc_func'])
file_prefix = file_prefix + sim_config['sim_name']
if config['close_daily']:
file_prefix = file_prefix + 'daily_'
config['file_prefix'] = file_prefix
summary_df = pd.DataFrame()
fname = config['file_prefix'] + 'summary.csv'
if os.path.isfile(fname):
summary_df = pd.DataFrame.from_csv(fname)
for asset in sim_list:
file_prefix = config['file_prefix'] + '_' + asset + '_'
fname = file_prefix + 'stats.json'
output = {}
if os.path.isfile(fname):
with open(fname, 'r') as fp:
output = json.load(fp)
if len(output.keys()) < len(scenarios):
if asset in sim_start_dict:
start_date = max(sim_start_dict[asset], config['start_date'])
else:
start_date = config['start_date']
if 'offset' in sim_config:
config['offset'] = sim_config['offset'] * trade_offset_dict[asset]
else:
config['offset'] = trade_offset_dict[asset]
config['marginrate'] = ( sim_margin_dict[asset], sim_margin_dict[asset])
config['nearby'] = 1
config['rollrule'] = '-50b'
config['exit_min'] = 2112
config['no_trade_set'] = range(300, 301) + range(1500, 1501) + range(2059, 2100)
if asset in ['cu', 'al', 'zn']:
config['nearby'] = 3
config['rollrule'] = '-1b'
elif asset in ['IF', 'IH', 'IC']:
config['rollrule'] = '-2b'
config['no_trade_set'] = range(1515, 1520) + range(2110, 2115)
elif asset in ['au', 'ag']:
config['rollrule'] = '-25b'
elif asset in ['TF', 'T']:
config['rollrule'] = '-20b'
config['no_trade_set'] = range(1515, 1520) + range(2110, 2115)
config['no_trade_set'] = []
nearby = config['nearby']
rollrule = config['rollrule']
if nearby > 0:
mdf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
mdf = cleanup_mindata(mdf, asset)
if 'need_daily' in sim_config:
ddf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'd', need_shift=True)
config['ddf'] = ddf
for ix, s in enumerate(scenarios):
fname1 = file_prefix + str(ix) + '_trades.csv'
fname2 = file_prefix + str(ix) + '_dailydata.csv'
if os.path.isfile(fname1) and os.path.isfile(fname2):
continue
for key, seq in zip(sim_config['scen_keys'], s):
config[key] = sim_config[key][seq]
df = mdf.copy(deep = True)
(res, closed_trades, ts) = run_sim( df, config)
res.update(dict(zip(sim_config['scen_keys'], s)))
res['asset'] = asset
output[ix] = res
print 'saving results for asset = %s, scen = %s' % (asset, str(ix))
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname1)
ts.to_csv(fname2)
fname = file_prefix + 'stats.json'
try:
with open(fname, 'w') as ofile:
json.dump(output, ofile)
except:
continue
res = pd.DataFrame.from_dict(output, orient = 'index')
res.index.name = 'scenario'
res = res.sort(columns = ['sharp_ratio'], ascending=False)
res = res.reset_index()
res.set_index(['asset', 'scenario'])
out_res = res[outcol_list]
if len(summary_df) == 0:
summary_df = out_res[:10].copy(deep = True)
else:
summary_df = summary_df.append(out_res[:10])
fname = config['file_prefix'] + 'summary.csv'
summary_df.to_csv(fname)
return
rollrule = config['rollrule']
file_prefix = config['file_prefix'] + '_' + asset + '_'
df = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq):
for iy, win in enumerate(windows):
idx = ix*10+iy
config['win'] = win
(res, closed_trades, ts) = aberration_sim( xdf, mdf, , config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def aberration_sim( xdf, mdf, config):
marginrate = config['marginrate']
offset = config['offset']
win = config['win']
start_equity = config['capital']