def get_eq_params(capital, ts, recap=False):
BEG = date_from_timestamp(ts[0])
END = date_from_timestamp(ts[-1])
years = (END - BEG).days / 365
dd = get_draw_down(capital, recap)
if recap:
rets = (capital[1:] - capital[:-1]) / capital[:-1]
sharpe = get_sharpe_ratio(rets, years)
y_avg = (capital[-1] - capital[0]) / capital[0] / years
else:
rets = capital[1:] - capital[:-1]
sharpe = get_sharpe_ratio(rets, years)
y_avg = (capital[-1] - capital[0]) / years
return dd, sharpe, y_avg
def build_eq(ds: DataSource, predictions, eval_config: EvalConfig):
beg_data_idx, end_data_idx = ds.get_data_range(eval_config.BEG, eval_config.END)
if beg_data_idx is None or end_data_idx is None:
return None
data_points = end_data_idx - beg_data_idx
cash = 0
pos = 0
pos_px = 0
if eval_config.POS_STRATEGY == PosStrategy.MON_FRI:
pos_strategy = HoldMonFriPosStrategyDeprecated()
elif eval_config.POS_STRATEGY == PosStrategy.FRI_FRI:
pos_strategy = HoldFriFriPosStrategyDeprecated()
elif eval_config.POS_STRATEGY == PosStrategy.PERIODIC:
pos_strategy = PeriodicPosStrategyDeprecated(eval_config.TRADES_FREQ)
eq = np.zeros(data_points)
for idx in range(data_points):
data_idx = beg_data_idx + idx
p = predictions[idx]
curr_px = ds.get_a_c(data_idx)
date = date_from_timestamp(ds.get_ts(data_idx))
next_trading_date = None
if idx < data_points - 1:
next_trading_date = date_from_timestamp(ds.get_ts(data_idx + 1))
close_pos, open_pos = pos_strategy.decide(date, next_trading_date)
if close_pos:
rpl = calc_pl(pos,curr_px,pos_px, eval_config.SLIPPAGE)
cash += rpl
pos = 0
if open_pos:
pos_px = curr_px
pos = 1 / curr_px * np.sign(p)
urpl = calc_pl(pos,curr_px,pos_px, eval_config.SLIPPAGE)
nlv = cash + urpl
eq[idx] = nlv
return eq, ds.get_ts(beg_data_idx, end_data_idx)
def test_ma_curve(self):
# client = pymongo.MongoClient()
# db = client['predictions']
# collection = db['predictions']
EPOCH = 600
eval_config = get_eval_config_petri_whole_set()
total_days = (eval_config.END - eval_config.BEG).days + 1
tickers = snp.get_snp_hitorical_components_tickers()
total_tickers = len(tickers)
weak_predictors = []
#dirty hack
for ticker in tickers:
if os.path.exists("data/nets/bagging/%s/train_progress.csv" % ticker):
weak_predictors.append(ticker)
weak_predictors_num = len(weak_predictors)
ALGO_NAME = "MODEL_AVERAGING"
es_tickers = quandl_data.get_es_tickers()
if os.path.exists("data/es.npz"):
data = np.load("data/es.npz")
es_prices = data['es_prices']
es_trading_mask = data['es_trading_mask']
else:
total_es_tickers = len(es_tickers)
es_prices = np.zeros((total_es_tickers, total_days))
es_trading_mask = np.full((total_es_tickers, total_days), False)
es_ds = []
for (t, _), ticker_idx in zip(es_tickers, range(total_es_tickers)):
ds = quandl_data.EsDataSouce(t)
es_ds.append(ds)
close_pxs = ds.get_close_px()
dates = ds.get_dates()
if close_pxs is None:
continue
data_len = close_pxs.shape[0]
_prev_idx = 0
price = None
for i in range(data_len):
date = dates[dates.index[i]]
if eval_config.BEG <= date <= eval_config.END:
_idx = (date - eval_config.BEG).days
price = close_pxs[i]
# if _prev_idx == 0:
# _prev_idx = _idx
es_prices[ticker_idx, _prev_idx: _idx + 1] = price
es_trading_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
if price is not None:
es_prices[ticker_idx, _prev_idx:] = price
np.savez("data/es.npz",
es_prices=es_prices,
es_trading_mask=es_trading_mask,
)
vx_tickers = quandl_data.get_vx_tickers()
if os.path.exists("data/vx.npz"):
data = np.load("data/vx.npz")
vx_prices = data['vx_prices']
vx_trading_mask = data['vx_trading_mask']
else:
total_vx_tickers = len(vx_tickers)
vx_prices = np.zeros((total_vx_tickers, total_days))
vx_trading_mask = np.full((total_vx_tickers, total_days), False)
vx_ds = []
for (t, _), ticker_idx in zip(vx_tickers, range(total_vx_tickers)):
ds = quandl_data.VxDataSouce(t)
vx_ds.append(ds)
close_pxs = ds.get_close_px()
dates = ds.get_dates()
if close_pxs is None:
continue
data_len = close_pxs.shape[0]
_prev_idx = 0
price = None
for i in range(data_len):
date = dates[dates.index[i]]
if eval_config.BEG <= date <= eval_config.END:
_idx = (date - eval_config.BEG).days
price = close_pxs[i]
# if _prev_idx == 0:
# _prev_idx = _idx
vx_prices[ticker_idx, _prev_idx: _idx + 1] = price
vx_trading_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
if price is not None:
vx_prices[ticker_idx, _prev_idx:] = price
np.savez("data/vx.npz",
vx_prices=vx_prices,
vx_trading_mask=vx_trading_mask,
)
snp_history = snp.get_snp_history()
prices = np.zeros((total_tickers, total_days))
trading_mask = np.full((total_tickers, total_days), False)
snp_mask = np.full((total_tickers, total_days), False)
if os.path.exists("data/snp.npz"):
data = np.load("data/snp.npz")
prices = data['prices']
trading_mask = data['trading_mask']
snp_mask = data['snp_mask']
else:
for ticker, ticker_idx in zip(tickers, range(total_tickers)):
try:
ds = DataSource(ticker)
b, e = ds.get_data_range(eval_config.BEG, eval_config.END)
px = ds.get_a_c(b, e)
ts = ds.get_ts(b, e)
_prev_idx = 0
for j in range(ts.shape[0]):
price = px[j]
date = date_from_timestamp(ts[j])
_idx = (date - eval_config.BEG).days
prices[ticker_idx, _prev_idx: _idx + 1] = price
trading_mask[ticker_idx, _idx] = True
if snp_history.check_if_belongs(ticker, date):
snp_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
prices[ticker_idx, _prev_idx:] = price
except:
print("No data")
pass
np.savez("data/snp.npz",
prices=prices,
trading_mask=trading_mask,
snp_mask=snp_mask,
)
CV_TS = get_date_timestamp(known_dates.YR_07)
ts = np.zeros(total_days)
for i in range(total_days):
date = eval_config.BEG + datetime.timedelta(days=i)
ts[i] = get_date_timestamp(date)
after_2007_mask = ts >= CV_TS
traded_stocks_per_day = trading_mask[:, :].sum(0)
trading_day_mask = traded_stocks_per_day >= 30
trading_day_mask &= after_2007_mask
trading_day_idxs = np.nonzero(trading_day_mask)[0]
es_trader_futures_per_day = es_trading_mask[:, :].sum(0)
es_trading_day_mask = es_trader_futures_per_day >= 1
es_trading_day_mask &= after_2007_mask
es_trading_day_idxs = np.nonzero(es_trading_day_mask)[0]
total_es_trading_days = es_trading_day_idxs.shape[0]
vx_trader_futures_per_day = vx_trading_mask[:, :].sum(0)
vx_trading_day_mask = vx_trader_futures_per_day >= 1
vx_trading_day_mask &= after_2007_mask
vx_trading_day_idxs = np.nonzero(vx_trading_day_mask)[0]
total_vx_trading_days = vx_trading_day_idxs.shape[0]
es_active_contract_idx = np.full((total_days), -1, dtype=np.int)
es_close_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
es_open_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_active_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_close_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_open_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
es_active_idx = -1
vx_active_idx = -1
for i in range(total_days):
es_mask = es_trading_mask[:, i]
es_idxs = np.nonzero(es_mask)[0]
if es_idxs.shape[0] > 0:
new_es_active_idx = es_idxs[0]
if new_es_active_idx < es_active_idx:
_debug = 0
es_active_idx = new_es_active_idx
es_active_contract_idx[i] = es_active_idx
vx_mask = vx_trading_mask[:, i]
vx_idxs = np.nonzero(vx_mask)[0]
if vx_idxs.shape[0] > 0:
new_vx_active_idx = vx_idxs[0]
if new_vx_active_idx < vx_active_idx:
_debug = 0
vx_active_idx = new_vx_active_idx
vx_active_contract_idx[i] = vx_active_idx
es_close_pos_contract_idx[:] = es_active_contract_idx[:]
es_open_pos_contract_idx[:] = es_active_contract_idx[:]
vx_close_pos_contract_idx[:] = vx_active_contract_idx[:]
vx_open_pos_contract_idx[:] = vx_active_contract_idx[:]
ES_ROLL_DAYS = 1
VX_ROLL_DAYS = 1
prev_day_idx = None
for i in range(total_es_trading_days - ES_ROLL_DAYS):
day_idx = es_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = es_trading_day_idxs[i + ES_ROLL_DAYS]
es_close_pos_contract_idx[prev_day_idx:day_idx + 1] = es_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
prev_day_idx = None
for i in range(total_vx_trading_days - VX_ROLL_DAYS):
day_idx = vx_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = vx_trading_day_idxs[i + VX_ROLL_DAYS]
vx_close_pos_contract_idx[prev_day_idx:day_idx + 1] = vx_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
ES_ROLL_DAYS += 1
VX_ROLL_DAYS += 1
prev_day_idx = None
for i in range(total_es_trading_days - ES_ROLL_DAYS):
day_idx = es_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = es_trading_day_idxs[i + ES_ROLL_DAYS]
es_open_pos_contract_idx[prev_day_idx:day_idx + 1] = es_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
prev_day_idx = None
for i in range(total_vx_trading_days - VX_ROLL_DAYS):
day_idx = vx_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = vx_trading_day_idxs[i + VX_ROLL_DAYS]
vx_open_pos_contract_idx[prev_day_idx:day_idx + 1] = vx_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
def calc_pl_simple(pos, curr_px, pos_px, price_step):
return pos * ((curr_px - np.sign(pos) * price_step) - (pos_px + np.sign(pos) * price_step))
def calc_pl(pos, curr_px, pos_px, slippage):
return np.sum(pos * (curr_px * (1 - np.sign(pos) * slippage) - pos_px * (1 + np.sign(pos) * slippage)))
if eval_config.POS_STRATEGY == PosStrategy.MON_FRI:
pos_strategy = HoldMonFriPosStrategy()
elif eval_config.POS_STRATEGY == PosStrategy.FRI_FRI:
pos_strategy = HoldFriFriPosStrategy()
elif eval_config.POS_STRATEGY == PosStrategy.PERIODIC:
pos_strategy = PeriodicPosStrategy(eval_config.TRADES_FREQ)
pos_strategy = HoldMonFriPredictMonPosStrategy()
# pos_strategy = HoldFriFriPosStrategy()
# pos_strategy = HoldMonFriPosStrategy()
# pos_strategy = HoldAlwaysAndRebalance()
# pos_strategy = HoldMonMonPosStrategy()
class RollPosStrategy(object):
def __init__(self):
self.in_pos = False
def decide(self, close_contract_idx, open_contract_idx):
# to open initial position
if not self.in_pos:
self.in_pos = True
return True, True
if close_contract_idx != open_contract_idx:
return True, True
return False, False
es_pos_strategy = RollPosStrategy()
vx_pos_strategy = RollPosStrategy()
VX_LAST_TRADING_DATE_BEFORE_RESCALE = datetime.datetime.strptime('2007-03-23', '%Y-%m-%d').date()
SELECTION = 5
RECAP = False
GS = [(1, 0, 0)]
HIDE_PLOT = False
if HIDE_PLOT:
plots.iof()
fig = None
ax = None
PRINT_TRADES = False
trades = []
ES_PRICE_STEP = 5
VX_PRICE_STEP = 0.05
PRINT_PERIODIC_STAT = True
if PRINT_PERIODIC_STAT:
INIT_NLV = 1000000
LEVERAGE = 1
RECAP = False
BY_QUARTER = False
PRINT_WEEKLY_PL_CHANGES = False
if PRINT_WEEKLY_PL_CHANGES:
weekly_balance = []
open_nlv = None
open_date = None
# # NetChange_MonFri
# weekly_balance_file_name = "data/net_change_mon_fri.csv"
# CH_COL_NAME = "NetChange_MonFri"
# OPEN_DATE_COL_NAME = "w beg"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackMonFriStrategy()
# pos_strategy = HoldMonFriPredictMonPosStrategy()
# GS = [(1, 0, 0)]
# # NetChange_FriFri
# weekly_balance_file_name = "data/net_change_fri_fri.csv"
# CH_COL_NAME = "NetChange_FriFri"
# OPEN_DATE_COL_NAME = "prev w end"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackFriFriStrategy()
# pos_strategy = HoldFriFriPosStrategy()
# GS = [(1, 0, 0)]
# NetChange_FriFri rebalance Mon
weekly_balance_file_name = "data/net_change_fri_fri_rebalance_mon.csv"
CH_COL_NAME = "NetChange_FriFri_Rebalance_Mon"
OPEN_DATE_COL_NAME = "prev w end"
CLOSE_DATE_COL_NAME = "w end"
track_strategy = TrackFriFriStrategy()
pos_strategy = HoldFriFriRebalanceMonPosStrategy()
GS = [(1, 0, 0)]
# # EsChange_MonFri
# weekly_balance_file_name = "data/es_change_mon_fri.csv"
# CH_COL_NAME = "EsChange_MonFri"
# OPEN_DATE_COL_NAME = "w beg"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackMonFriStrategy()
# GS = [(0, 1, 0)]
# ES_PRICE_STEP = 0
# # EsChange_FriFri
# weekly_balance_file_name = "data/es_change_fri_fri.csv"
# CH_COL_NAME = "EsChange_FriFri"
# OPEN_DATE_COL_NAME = "prev w end"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackFriFriStrategy()
# GS = [(0, 1, 0)]
# ES_PRICE_STEP = 0
for NET_BET, ES_BET, VX_BET in GS:
linear_combination = "NET_%d_ES_%d_VX_%d" % (NET_BET, ES_BET, VX_BET)
Z = abs(NET_BET) + abs(ES_BET) + abs(VX_BET)
NET_BET /= Z
ES_BET /= Z
VX_BET /= Z
linear_combination_normalizer = "NET_%.2f_ES_%.2f_VX_%.2f" % (NET_BET, ES_BET, VX_BET)
cash = 1
pos = np.zeros((total_tickers))
pos_px = np.zeros((total_tickers))
eq = np.zeros(total_days)
es_pos = 0
es_pos_px = 0
vx_pos = 0
vx_pos_px = 0
td_idx = 0
for i in range(total_days):
date = eval_config.BEG + datetime.timedelta(days=i)
es_trading_day = es_trading_day_mask[i]
es_close_contract_idx = es_close_pos_contract_idx[i]
es_open_contract_idx = es_open_pos_contract_idx[i]
es_close_px = es_prices[es_close_contract_idx, i]
es_open_px = es_prices[es_open_contract_idx, i]
es_close_pos = False
es_open_pos = False
if es_trading_day:
es_close_pos, es_open_pos = es_pos_strategy.decide(es_close_contract_idx, es_open_contract_idx)
if es_close_pos:
rpl = calc_pl_simple(es_pos, es_close_px, es_pos_px, ES_PRICE_STEP)
cash += rpl
es_pos = 0
if es_open_pos:
if RECAP:
es_pos = abs(ES_BET) * eq[i - 1] / es_open_px * np.sign(ES_BET)
# es_pos = abs(ES_BET) * cash / es_open_px * np.sign(ES_BET)
else:
es_pos = abs(ES_BET) / es_open_px * np.sign(ES_BET)
es_pos_px = es_open_px
es_close_px = es_open_px
if date == datetime.datetime.strptime('2007-04-13', '%Y-%m-%d').date():
_debug = 0
vx_trading_day = vx_trading_day_mask[i]
vx_close_contract_idx = vx_close_pos_contract_idx[i]
vx_open_contract_idx = vx_open_pos_contract_idx[i]
vx_close_px = vx_prices[vx_close_contract_idx, i]
if vx_tickers[vx_close_contract_idx][0] == 'VXJ2007' and date > VX_LAST_TRADING_DATE_BEFORE_RESCALE:
vx_close_px *= 10
vx_open_px = vx_prices[vx_open_contract_idx, i]
vx_close_pos = False
vx_open_pos = False
if vx_trading_day:
vx_close_pos, vx_open_pos = vx_pos_strategy.decide(vx_close_contract_idx, vx_open_contract_idx)
if vx_close_pos:
rpl = calc_pl_simple(vx_pos, vx_close_px, vx_pos_px, VX_PRICE_STEP)
cash += rpl
vx_pos = 0
if vx_open_pos:
if RECAP:
vx_pos = abs(VX_BET) * eq[i - 1] / vx_open_px * np.sign(VX_BET)
# vx_pos = abs(VX_BET) * cash / vx_open_px * np.sign(VX_BET)
else:
vx_pos = abs(VX_BET) / vx_open_px * np.sign(VX_BET)
vx_pos_px = vx_open_px
vx_close_px = vx_open_px
curr_px = prices[:, i]
tradeable = trading_mask[:, i]
in_snp = snp_mask[:, i]
trading_day = trading_day_mask[i]
if trading_day:
next_trading_date = None
if td_idx + 1 < trading_day_idxs.shape[0]:
next_trading_date = eval_config.BEG + datetime.timedelta(
days=trading_day_idxs[td_idx + 1].item())
if trading_day:
predict, close_pos, open_pos = pos_strategy.decide(date, next_trading_date)
# if date <= datetime.datetime.strptime('2008-10-01', '%Y-%m-%d').date():
# predict = False
# close_pos = False
# open_pos = False
if predict:
pos_mask = tradeable & in_snp
weights = np.zeros((total_tickers))
s_date = date.strftime('%Y-%m-%d')
npz_file_name = "data/eval/dates/petri/%s.npz" % s_date
if os.path.exists(npz_file_name):
archived_data = np.load(npz_file_name)
predictions = archived_data['predictions']
else:
predictions = np.zeros((total_tickers, weak_predictors_num))
pred_df = pd.read_csv("data/eval/dates/petri/%s.csv" % s_date)
for index, row in pred_df.iterrows():
p = row.prediction
w_p = row.weak_predictor
t = row.ticker
try:
w_p_i = weak_predictors.index(w_p)
t_i = tickers.index(t)
# w_p_i = 0
# t_i = 0
predictions[t_i, w_p_i] = p
except:
pass
np.savez(npz_file_name, predictions=predictions, pos_mask=pos_mask)
# now we filled all predictions - find out cov matrix
pos_predictions = predictions[pos_mask,:]
cov = np.cov(pos_predictions)
exp = np.mean(pos_predictions, axis=1)
balancer = capm.Capm(48, SELECTION, exp, cov)
balancer.init()
w = balancer.fit_weights()
weights[pos_mask] = w
if close_pos:
rpl = calc_pl(pos, curr_px, pos_px, eval_config.SLIPPAGE)
cash += rpl
if PRINT_TRADES:
stk_idxs = np.nonzero(pos)[0]
for l in range(stk_idxs.shape[0]):
stk_idx = stk_idxs[l]
trade_ticker = tickers[stk_idx]
trade_px = curr_px[stk_idx]
trade_pos = -pos[stk_idx]
trades.append({
'date': date,
'ticker': trade_ticker,
'pos': trade_pos,
'px': trade_px,
'action': 'close',
'cash flow': -trade_pos * trade_px,
'capital': cash if l == stk_idxs.shape[0] - 1 else 0
})
pos[:] = 0
if open_pos:
pos_mask = tradeable & in_snp
num_stks = np.sum(pos_mask)
if RECAP:
pos[pos_mask] = NET_BET * eq[i - 1] * weights[pos_mask] / curr_px[pos_mask]
else:
pos[pos_mask] = NET_BET * weights[pos_mask] / curr_px[pos_mask]
pos_px = curr_px
if PRINT_TRADES:
stk_idxs = np.nonzero(pos)[0]
for l in range(stk_idxs.shape[0]):
stk_idx = stk_idxs[l]
trade_ticker = tickers[stk_idx]
trade_px = curr_px[stk_idx]
trade_pos = pos[stk_idx]
trades.append({
'date': date,
'ticker': trade_ticker,
'pos': trade_pos,
'px': trade_px,
'action': 'open',
'cash flow': -trade_pos * trade_px,
'capital': 0
})
td_idx += 1
urpl = calc_pl(pos, curr_px, pos_px, eval_config.SLIPPAGE)
es_urpl = calc_pl_simple(es_pos, es_close_px, es_pos_px, ES_PRICE_STEP)
vx_urpl = calc_pl_simple(vx_pos, vx_close_px, vx_pos_px, VX_PRICE_STEP)
nlv = cash + urpl + es_urpl + vx_urpl
eq[i] = nlv
ts[i] = get_date_timestamp(date)
if PRINT_WEEKLY_PL_CHANGES:
if trading_day:
track_close_pos, track_open_pos = track_strategy.decide(date, next_trading_date)
if track_close_pos:
close_nlv = nlv
close_date = date
weekly_balance.append((
close_nlv - open_nlv,
open_date,
close_date))
if track_open_pos:
open_nlv = nlv
open_date = date
if PRINT_PERIODIC_STAT:
test_eq = eq[after_2007_mask]
test_ts = ts[after_2007_mask]
period_beg_idx = 0
period_start_nlv_idx = 0
stat = []
for i in range(test_ts.shape[0]):
date = date_from_timestamp(test_ts[i])
period_beg_date = date_from_timestamp(test_ts[period_beg_idx])
period_start_nlv_date = date_from_timestamp(test_ts[period_start_nlv_idx])
if BY_QUARTER:
def check_new_period(period_beg_date, date):
quarter_beg_date = (period_beg_date.month - 1) // 3
quarter_date = (date.month - 1) // 3
if quarter_beg_date != quarter_date:
return True
return False
else:
def check_new_period(period_beg_date, date):
return period_beg_date.month != date.month
if check_new_period(period_beg_date, date):
min_equity = INIT_NLV * np.min(test_eq[period_start_nlv_idx:i])
nlv_beg = INIT_NLV * test_eq[period_start_nlv_idx]
nlv_end = INIT_NLV * test_eq[i - 1]
pct_ch = (nlv_end - nlv_beg) / nlv_beg
dd = (min_equity - nlv_beg) / nlv_beg
period_end_date = date_from_timestamp(test_ts[i - 1])
period_beg_idx = i
period_start_nlv_idx = i - 1
stat.append({
"date beg": period_start_nlv_date,
"date end": period_end_date,
"nlv beg": nlv_beg,
"nlv end": nlv_end,
"dd": dd,
"change pct": pct_ch,
})
stat_df = pd.DataFrame(stat)
stat_df = stat_df[['date beg', 'date end', 'nlv beg', 'nlv end', 'dd', 'change pct']]
stat_df.to_csv(
"data/%s_%s_stat.csv" % (linear_combination_normalizer, "quaterly" if BY_QUARTER else "monthly"),
index=False)
# plot eq
folder_path, file_path = folders.get_adaptive_plot_path(ALGO_NAME, linear_combination, EPOCH)
folders.create_dir(folder_path)
# fig = plots.plot_eq("total", eq[after_2007_mask], ts[after_2007_mask])
# fig.savefig(file_path)
if fig is None:
fig, ax = plots.create_eq_ax("equity curve")
plots.plot_serie(ax, eq[after_2007_mask], ts[after_2007_mask])
if HIDE_PLOT:
plots.close_fig(fig)
test = False
train = False
test_idxs = np.nonzero(ts > CV_TS)[0]
if test_idxs.shape[0] > 0:
test = True
train_idxs = np.nonzero(ts <= CV_TS)[0]
if train_idxs.shape[0] > 0:
train = True
test_dd = 0
test_sharpe = 0
test_y_avg = 0
train_dd = 0
train_sharpe = 0
train_y_avg = 0
train = False
# save stat
if test:
test_dd, test_sharpe, test_y_avg = get_eq_params(eq[test_idxs], ts[test_idxs], RECAP)
print("%s Test dd: %.2f%% y avg: %.2f%% sharpe: %.2f" % (
linear_combination_normalizer, test_dd * 100, test_y_avg * 100, test_sharpe))
if train:
train_dd, train_sharpe, train_y_avg = get_eq_params(eq[train_idxs], ts[train_idxs], RECAP)
print("%s Train dd: %.2f%% y avg: %.2f%% sharpe: %.2f" % (
linear_combination_normalizer, train_dd * 100, train_y_avg * 100, train_sharpe))
if PRINT_TRADES:
pd.DataFrame(trades).to_csv("data/trades.csv", index=False)
if PRINT_WEEKLY_PL_CHANGES:
components = [[*x] for x in zip(*weekly_balance)]
change = components[0]
open_dates = components[1]
close_dates = components[2]
pd.DataFrame({
CH_COL_NAME: change,
OPEN_DATE_COL_NAME: open_dates,
CLOSE_DATE_COL_NAME: close_dates
}).to_csv(weekly_balance_file_name, index=False)
if not HIDE_PLOT:
plots.show_plots()
def build_time_axis(ts):
dt = []
for raw_dt in np.nditer(ts):
dt.append(date_from_timestamp(raw_dt))
return dt
def test_ma_curve(self):
EPOCH = 600
eval_config = get_eval_config_petri_whole_set()
total_days = (eval_config.END - eval_config.BEG).days + 1
tickers = snp.get_snp_hitorical_components_tickers()
total_tickers = len(tickers)
ALGO_NAME = "MODEL_AVERAGING"
es_tickers = quandl_data.get_es_tickers()
if os.path.exists("data/es.npz"):
data = np.load("data/es.npz")
es_prices = data['es_prices']
es_trading_mask = data['es_trading_mask']
else:
total_es_tickers = len(es_tickers)
es_prices = np.zeros((total_es_tickers, total_days))
es_trading_mask = np.full((total_es_tickers, total_days), False)
es_ds = []
for (t, _), ticker_idx in zip(es_tickers, range(total_es_tickers)):
ds = quandl_data.EsDataSouce(t)
es_ds.append(ds)
close_pxs = ds.get_close_px()
dates = ds.get_dates()
if close_pxs is None:
continue
data_len = close_pxs.shape[0]
_prev_idx = 0
price = None
for i in range(data_len):
date = dates[dates.index[i]]
if eval_config.BEG <= date <= eval_config.END:
_idx = (date - eval_config.BEG).days
price = close_pxs[i]
# if _prev_idx == 0:
# _prev_idx = _idx
es_prices[ticker_idx, _prev_idx:_idx + 1] = price
es_trading_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
if price is not None:
es_prices[ticker_idx, _prev_idx:] = price
np.savez(
"data/es.npz",
es_prices=es_prices,
es_trading_mask=es_trading_mask,
)
vx_tickers = quandl_data.get_vx_tickers()
if os.path.exists("data/vx.npz"):
data = np.load("data/vx.npz")
vx_prices = data['vx_prices']
vx_trading_mask = data['vx_trading_mask']
else:
total_vx_tickers = len(vx_tickers)
vx_prices = np.zeros((total_vx_tickers, total_days))
vx_trading_mask = np.full((total_vx_tickers, total_days), False)
vx_ds = []
for (t, _), ticker_idx in zip(vx_tickers, range(total_vx_tickers)):
ds = quandl_data.VxDataSouce(t)
vx_ds.append(ds)
close_pxs = ds.get_close_px()
dates = ds.get_dates()
if close_pxs is None:
continue
data_len = close_pxs.shape[0]
_prev_idx = 0
price = None
for i in range(data_len):
date = dates[dates.index[i]]
if eval_config.BEG <= date <= eval_config.END:
_idx = (date - eval_config.BEG).days
price = close_pxs[i]
# if _prev_idx == 0:
# _prev_idx = _idx
vx_prices[ticker_idx, _prev_idx:_idx + 1] = price
vx_trading_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
if price is not None:
vx_prices[ticker_idx, _prev_idx:] = price
np.savez(
"data/vx.npz",
vx_prices=vx_prices,
vx_trading_mask=vx_trading_mask,
)
snp_history = snp.get_snp_history()
prices = np.zeros((total_tickers, total_days))
trading_mask = np.full((total_tickers, total_days), False)
snp_mask = np.full((total_tickers, total_days), False)
if os.path.exists("data/snp.npz"):
data = np.load("data/snp.npz")
prices = data['prices']
trading_mask = data['trading_mask']
snp_mask = data['snp_mask']
else:
for ticker, ticker_idx in zip(tickers, range(total_tickers)):
try:
ds = DataSource(ticker)
b, e = ds.get_data_range(eval_config.BEG, eval_config.END)
px = ds.get_a_c(b, e)
ts = ds.get_ts(b, e)
_prev_idx = 0
for j in range(ts.shape[0]):
price = px[j]
date = date_from_timestamp(ts[j])
_idx = (date - eval_config.BEG).days
prices[ticker_idx, _prev_idx:_idx + 1] = price
trading_mask[ticker_idx, _idx] = True
if snp_history.check_if_belongs(ticker, date):
snp_mask[ticker_idx, _idx] = True
_prev_idx = _idx + 1
prices[ticker_idx, _prev_idx:] = price
except:
print("No data")
pass
np.savez(
"data/snp.npz",
prices=prices,
trading_mask=trading_mask,
snp_mask=snp_mask,
)
predictions = np.zeros((total_tickers, total_days))
npz_folder, npz_file = folders.get_adaptive_compressed_predictions_path(
ALGO_NAME)
folders.create_dir(npz_folder)
if os.path.exists(npz_file):
data = np.load(npz_file)
predictions = data['predictions']
else:
for ticker, ticker_idx in zip(tickers, range(total_tickers)):
print("Loading predictions for %s" % ticker)
try:
# file_path = "data/eval/petri/AVERAGE/eval/%s/prediction/%s_164.csv" % (ticker, ticker)
folder_path, file_path = folders.get_adaptive_prediction_path(
ALGO_NAME, eval_config, ticker, EPOCH)
df = pd.read_csv(file_path)
p = df.prediction.values
ts = df.ts.values
for j in range(p.shape[0]):
date = date_from_timestamp(ts[j])
_idx = (date - eval_config.BEG).days
_prediction = p[j]
predictions[ticker_idx, _idx] = _prediction
except:
print("No data")
pass
np.savez(npz_file, predictions=predictions)
CV_TS = get_date_timestamp(known_dates.YR_07)
ts = np.zeros(total_days)
for i in range(total_days):
date = eval_config.BEG + datetime.timedelta(days=i)
ts[i] = get_date_timestamp(date)
after_2007_mask = ts >= CV_TS
traded_stocks_per_day = trading_mask[:, :].sum(0)
trading_day_mask = traded_stocks_per_day >= 30
trading_day_mask &= after_2007_mask
trading_day_idxs = np.nonzero(trading_day_mask)[0]
es_trader_futures_per_day = es_trading_mask[:, :].sum(0)
es_trading_day_mask = es_trader_futures_per_day >= 1
es_trading_day_mask &= after_2007_mask
es_trading_day_idxs = np.nonzero(es_trading_day_mask)[0]
total_es_trading_days = es_trading_day_idxs.shape[0]
vx_trader_futures_per_day = vx_trading_mask[:, :].sum(0)
vx_trading_day_mask = vx_trader_futures_per_day >= 1
vx_trading_day_mask &= after_2007_mask
vx_trading_day_idxs = np.nonzero(vx_trading_day_mask)[0]
total_vx_trading_days = vx_trading_day_idxs.shape[0]
es_active_contract_idx = np.full((total_days), -1, dtype=np.int)
es_close_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
es_open_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_active_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_close_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
vx_open_pos_contract_idx = np.full((total_days), -1, dtype=np.int)
es_active_idx = -1
vx_active_idx = -1
for i in range(total_days):
es_mask = es_trading_mask[:, i]
es_idxs = np.nonzero(es_mask)[0]
if es_idxs.shape[0] > 0:
new_es_active_idx = es_idxs[0]
if new_es_active_idx < es_active_idx:
_debug = 0
es_active_idx = new_es_active_idx
es_active_contract_idx[i] = es_active_idx
vx_mask = vx_trading_mask[:, i]
vx_idxs = np.nonzero(vx_mask)[0]
if vx_idxs.shape[0] > 0:
new_vx_active_idx = vx_idxs[0]
if new_vx_active_idx < vx_active_idx:
_debug = 0
vx_active_idx = new_vx_active_idx
vx_active_contract_idx[i] = vx_active_idx
es_close_pos_contract_idx[:] = es_active_contract_idx[:]
es_open_pos_contract_idx[:] = es_active_contract_idx[:]
vx_close_pos_contract_idx[:] = vx_active_contract_idx[:]
vx_open_pos_contract_idx[:] = vx_active_contract_idx[:]
ES_ROLL_DAYS = 1
VX_ROLL_DAYS = 1
prev_day_idx = None
for i in range(total_es_trading_days - ES_ROLL_DAYS):
day_idx = es_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = es_trading_day_idxs[i + ES_ROLL_DAYS]
es_close_pos_contract_idx[prev_day_idx:day_idx +
1] = es_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
prev_day_idx = None
for i in range(total_vx_trading_days - VX_ROLL_DAYS):
day_idx = vx_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = vx_trading_day_idxs[i + VX_ROLL_DAYS]
vx_close_pos_contract_idx[prev_day_idx:day_idx +
1] = vx_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
ES_ROLL_DAYS += 1
VX_ROLL_DAYS += 1
prev_day_idx = None
for i in range(total_es_trading_days - ES_ROLL_DAYS):
day_idx = es_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = es_trading_day_idxs[i + ES_ROLL_DAYS]
es_open_pos_contract_idx[prev_day_idx:day_idx +
1] = es_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
prev_day_idx = None
for i in range(total_vx_trading_days - VX_ROLL_DAYS):
day_idx = vx_trading_day_idxs[i]
if prev_day_idx is None:
prev_day_idx = day_idx
roll_day_idx = vx_trading_day_idxs[i + VX_ROLL_DAYS]
vx_open_pos_contract_idx[prev_day_idx:day_idx +
1] = vx_active_contract_idx[roll_day_idx]
prev_day_idx = day_idx + 1
def calc_pl_simple(pos, curr_px, pos_px, price_step):
return pos * ((curr_px - np.sign(pos) * price_step) -
(pos_px + np.sign(pos) * price_step))
def calc_pl(pos, curr_px, pos_px, slippage):
return np.sum(pos *
(curr_px * (1 - np.sign(pos) * slippage) - pos_px *
(1 + np.sign(pos) * slippage)))
if eval_config.POS_STRATEGY == PosStrategy.MON_FRI:
pos_strategy = HoldMonFriPosStrategy()
elif eval_config.POS_STRATEGY == PosStrategy.FRI_FRI:
pos_strategy = HoldFriFriPosStrategy()
elif eval_config.POS_STRATEGY == PosStrategy.PERIODIC:
pos_strategy = PeriodicPosStrategy(eval_config.TRADES_FREQ)
pos_strategy = HoldMonFriPredictMonPosStrategy()
# pos_strategy = HoldFriFriPosStrategy()
# pos_strategy = HoldMonFriPosStrategy()
# pos_strategy = HoldAlwaysAndRebalance()
# pos_strategy = HoldMonMonPosStrategy()
class RollPosStrategy(object):
def __init__(self):
self.in_pos = False
def decide(self, close_contract_idx, open_contract_idx):
# to open initial position
if not self.in_pos:
self.in_pos = True
return True, True
if close_contract_idx != open_contract_idx:
return True, True
return False, False
es_pos_strategy = RollPosStrategy()
vx_pos_strategy = RollPosStrategy()
VX_LAST_TRADING_DATE_BEFORE_RESCALE = datetime.datetime.strptime(
'2007-03-23', '%Y-%m-%d').date()
SELECTION = 5
RECAP = False
NET_BET = 0
ES_BET = 1
VX_BET = 0
# GS = [(0,-10,-10),(0,-100,-100)]
# GS = [(0, -100, -100), (0, -10, -10)]
# GS = [(1, 0, 0),(0,-1,0),(0,0,-1)]
# GS = [(1, 0, -1)]
# GS = []
# for i in np.arange(0, 101, 10):
# for j in np.arange(-100,101,10):
# for k in np.arange(-100,101,10):
# if i == 0 and j == 0 and k == 0:
# continue
# NET_BET = i
# ES_BET = j
# VX_BET = k
# GS.append((NET_BET, ES_BET, VX_BET))
# GS = []
# for j in np.arange(-400, 401, 10):
# for k in np.arange(-150, 101, 10):
# NET_BET = 100
# ES_BET = j
# VX_BET = k
# if NET_BET == 0 and ES_BET == 0 and VX_BET == 0:
# continue
# GS.append((NET_BET, ES_BET, VX_BET))
GS = []
for k in np.arange(-100, 101, 10):
NET_BET = 0
ES_BET = -100
VX_BET = k
if NET_BET == 0 and ES_BET == 0 and VX_BET == 0:
continue
GS.append((NET_BET, ES_BET, VX_BET))
# GS = [(1, 0, -1), (0, -1, 0)]
# GS = [(1, 0, 0)]
GS = [
(100, -120, -40),
(100, -120, -70),
(100, -120, -50),
]
GS = [(1, 0, 0)]
DECLINE_LONG_FACTOR = 0.5
DECLINE_SHORT_FACTOR = 0.5
LONG_STOCKS = 5
SHORT_STOCKS = 5
LONG_PCT = 0.5
SHORT_PCT = 1 - LONG_PCT
# DECLINE_GS = [
# (0.5, 0.5, 5, 5),
# (0.5, 0.5, 5, 5),
# ]
DECLINE_GS = []
for SHORT_STOCKS in range(5, 51, 5):
for LONG_STOCKS in range(SHORT_STOCKS, 51, 5):
for DECLINE_LONG_FACTOR in np.arange(0, 1.01, 0.25):
for DECLINE_SHORT_FACTOR in np.arange(0, 1.01, 0.25):
DECLINE_GS.append(
(DECLINE_LONG_FACTOR, DECLINE_SHORT_FACTOR,
LONG_STOCKS, SHORT_STOCKS))
PRINT_DECLINE_GS = True
HIDE_PLOT = True
if HIDE_PLOT:
plots.iof()
fig = None
ax = None
PRINT_TRADES = False
trades = []
ES_PRICE_STEP = 5
VX_PRICE_STEP = 0.05
INIT_NLV = 1000000
PRINT_PERIODIC_STAT = False
BY_QUARTER = False
if PRINT_PERIODIC_STAT:
RECAP = True
PRINT_WEEKLY_PL_CHANGES = False
if PRINT_WEEKLY_PL_CHANGES:
weekly_balance = []
open_nlv = None
open_date = None
# # NetChange_MonFri
# weekly_balance_file_name = "data/net_change_mon_fri.csv"
# CH_COL_NAME = "NetChange_MonFri"
# OPEN_DATE_COL_NAME = "w beg"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackMonFriStrategy()
# pos_strategy = HoldMonFriPredictMonPosStrategy()
# GS = [(1, 0, 0)]
# # NetChange_FriFri
# weekly_balance_file_name = "data/net_change_fri_fri.csv"
# CH_COL_NAME = "NetChange_FriFri"
# OPEN_DATE_COL_NAME = "prev w end"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackFriFriStrategy()
# pos_strategy = HoldFriFriPosStrategy()
# GS = [(1, 0, 0)]
# NetChange_FriFri rebalance Mon
weekly_balance_file_name = "data/net_change_fri_fri_rebalance_mon.csv"
CH_COL_NAME = "NetChange_FriFri_Rebalance_Mon"
OPEN_DATE_COL_NAME = "prev w end"
CLOSE_DATE_COL_NAME = "w end"
track_strategy = TrackFriFriStrategy()
pos_strategy = HoldFriFriRebalanceMonPosStrategy()
GS = [(1, 0, 0)]
# # EsChange_MonFri
# weekly_balance_file_name = "data/es_change_mon_fri.csv"
# CH_COL_NAME = "EsChange_MonFri"
# OPEN_DATE_COL_NAME = "w beg"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackMonFriStrategy()
# GS = [(0, 1, 0)]
# ES_PRICE_STEP = 0
# # EsChange_FriFri
# weekly_balance_file_name = "data/es_change_fri_fri.csv"
# CH_COL_NAME = "EsChange_FriFri"
# OPEN_DATE_COL_NAME = "prev w end"
# CLOSE_DATE_COL_NAME = "w end"
# track_strategy = TrackFriFriStrategy()
# GS = [(0, 1, 0)]
# ES_PRICE_STEP = 0
for NET_BET, ES_BET, VX_BET in GS:
for DECLINE_LONG_FACTOR, DECLINE_SHORT_FACTOR, LONG_STOCKS, SHORT_STOCKS in DECLINE_GS:
LONG_PCT = LONG_STOCKS / (LONG_STOCKS + SHORT_STOCKS)
SHORT_PCT = SHORT_STOCKS / (LONG_STOCKS + SHORT_STOCKS)
decline_params = "DLF %03.0f%% DSF %03.0f%% LS %02d SS %02d" % (
DECLINE_LONG_FACTOR * 100, DECLINE_SHORT_FACTOR * 100,
LONG_STOCKS, SHORT_STOCKS)
prev_pos_mask = None
prev_px = None
linear_combination = "NET_%d_ES_%d_VX_%d" % (NET_BET, ES_BET,
VX_BET)
Z = abs(NET_BET) + abs(ES_BET) + abs(VX_BET)
NET_BET /= Z
ES_BET /= Z
VX_BET /= Z
linear_combination_normalizer = "NET_%.2f_ES_%.2f_VX_%.2f" % (
NET_BET, ES_BET, VX_BET)
cash = 1
pos = np.zeros((total_tickers))
pos_px = np.zeros((total_tickers))
eq = np.zeros(total_days)
es_pos = 0
es_pos_px = 0
vx_pos = 0
vx_pos_px = 0
td_idx = 0
for i in range(total_days):
date = eval_config.BEG + datetime.timedelta(days=i)
es_trading_day = es_trading_day_mask[i]
es_close_contract_idx = es_close_pos_contract_idx[i]
es_open_contract_idx = es_open_pos_contract_idx[i]
es_close_px = es_prices[es_close_contract_idx, i]
es_open_px = es_prices[es_open_contract_idx, i]
es_close_pos = False
es_open_pos = False
if es_trading_day:
es_close_pos, es_open_pos = es_pos_strategy.decide(
es_close_contract_idx, es_open_contract_idx)
if es_close_pos:
rpl = calc_pl_simple(es_pos, es_close_px, es_pos_px,
ES_PRICE_STEP)
cash += rpl
es_pos = 0
if es_open_pos:
if RECAP:
es_pos = abs(ES_BET) * eq[
i - 1] / es_open_px * np.sign(ES_BET)
# es_pos = abs(ES_BET) * cash / es_open_px * np.sign(ES_BET)
else:
es_pos = abs(ES_BET) / es_open_px * np.sign(ES_BET)
es_pos_px = es_open_px
es_close_px = es_open_px
if date == datetime.datetime.strptime(
'2007-04-13', '%Y-%m-%d').date():
_debug = 0
vx_trading_day = vx_trading_day_mask[i]
vx_close_contract_idx = vx_close_pos_contract_idx[i]
vx_open_contract_idx = vx_open_pos_contract_idx[i]
vx_close_px = vx_prices[vx_close_contract_idx, i]
if vx_tickers[vx_close_contract_idx][
0] == 'VXJ2007' and date > VX_LAST_TRADING_DATE_BEFORE_RESCALE:
vx_close_px *= 10
vx_open_px = vx_prices[vx_open_contract_idx, i]
vx_close_pos = False
vx_open_pos = False
if vx_trading_day:
vx_close_pos, vx_open_pos = vx_pos_strategy.decide(
vx_close_contract_idx, vx_open_contract_idx)
if vx_close_pos:
rpl = calc_pl_simple(vx_pos, vx_close_px, vx_pos_px,
VX_PRICE_STEP)
cash += rpl
vx_pos = 0
if vx_open_pos:
if RECAP:
vx_pos = abs(VX_BET) * eq[
i - 1] / vx_open_px * np.sign(VX_BET)
# vx_pos = abs(VX_BET) * cash / vx_open_px * np.sign(VX_BET)
else:
vx_pos = abs(VX_BET) / vx_open_px * np.sign(VX_BET)
vx_pos_px = vx_open_px
vx_close_px = vx_open_px
curr_px = prices[:, i]
tradeable = trading_mask[:, i]
in_snp = snp_mask[:, i]
trading_day = trading_day_mask[i]
if trading_day:
next_trading_date = None
if td_idx + 1 < trading_day_idxs.shape[0]:
next_trading_date = eval_config.BEG + datetime.timedelta(
days=trading_day_idxs[td_idx + 1].item())
if trading_day:
predict, close_pos, open_pos = pos_strategy.decide(
date, next_trading_date)
if predict:
prediction = predictions[:, i]
if close_pos:
rpl = calc_pl(pos, curr_px, pos_px,
eval_config.SLIPPAGE)
cash += rpl
if PRINT_TRADES:
stk_idxs = np.nonzero(pos)[0]
for l in range(stk_idxs.shape[0]):
stk_idx = stk_idxs[l]
trade_ticker = tickers[stk_idx]
trade_px = curr_px[stk_idx]
trade_pos = -pos[stk_idx]
trades.append({
'date':
date,
'ticker':
trade_ticker,
'pos':
trade_pos,
'px':
trade_px,
'action':
'close',
'cash flow':
-trade_pos * trade_px,
'capital':
cash if l == stk_idxs.shape[0] -
1 else 0
})
pos[:] = 0
if open_pos:
pos_mask = tradeable & in_snp
if prev_pos_mask is None:
prev_pos_mask = pos_mask
if prev_px is None:
prev_px = curr_px
pos_mask &= prev_pos_mask
decline = np.zeros(pos_mask.shape)
decline[pos_mask] = (
curr_px[pos_mask] -
prev_px[pos_mask]) / prev_px[pos_mask]
if DECLINE_LONG_FACTOR != 1:
net_long_mask = np.full(pos_mask.shape,
False,
dtype=np.bool)
net_long_mask[
pos_mask] = prediction[pos_mask] > 0
else:
net_long_mask = np.full(pos_mask.shape,
True,
dtype=np.bool)
if DECLINE_SHORT_FACTOR != 1:
net_short_mask = np.full(pos_mask.shape,
False,
dtype=np.bool)
net_short_mask[
pos_mask] = prediction[pos_mask] < 0
else:
net_short_mask = np.full(pos_mask.shape,
True,
dtype=np.bool)
if DECLINE_LONG_FACTOR != 0:
decline_long_mask = np.full(pos_mask.shape,
False,
dtype=np.bool)
decline_long_mask[
pos_mask] = decline[pos_mask] < 0
else:
decline_long_mask = np.full(pos_mask.shape,
True,
dtype=np.bool)
if DECLINE_SHORT_FACTOR != 0:
decline_short_mask = np.full(pos_mask.shape,
False,
dtype=np.bool)
decline_short_mask[
pos_mask] = decline[pos_mask] > 0
else:
decline_short_mask = np.full(pos_mask.shape,
True,
dtype=np.bool)
long_pos_mask = net_long_mask & decline_long_mask
short_pos_mask = net_short_mask & decline_short_mask
long_metric = np.zeros(pos_mask.shape)
short_metric = np.zeros(pos_mask.shape)
long_metric[long_pos_mask] = DECLINE_LONG_FACTOR * -decline[long_pos_mask] + (
1 - DECLINE_LONG_FACTOR) * \
prediction[
long_pos_mask]
short_metric[short_pos_mask] = DECLINE_SHORT_FACTOR * -decline[short_pos_mask] + (
1 - DECLINE_SHORT_FACTOR) * \
prediction[
short_pos_mask]
long_candidates = np.sum(long_pos_mask)
long_metric_sorted = np.sort(long_metric)
long_bound_idx = max(-LONG_STOCKS,
-long_candidates)
long_bound = long_metric_sorted[long_bound_idx]
if long_candidates == 0:
long_bound = np.iinfo(np.int32).max
short_candidates = np.sum(short_pos_mask)
short_metric_sorted = np.sort(short_metric)
short_bound_idx = min(SHORT_STOCKS - 1,
short_candidates - 1)
short_bound = short_metric_sorted[short_bound_idx]
if short_candidates == 0:
short_bound = np.iinfo(np.int32).max
long_pos_mask &= long_metric >= long_bound
short_pos_mask &= short_metric <= short_bound
long_stocks = np.sum(long_pos_mask)
short_stocks = np.sum(short_pos_mask)
num_stks = long_stocks + short_stocks
if RECAP:
if long_stocks != 0:
pos[long_pos_mask] = LONG_PCT * NET_BET * eq[
i - 1] / long_stocks / curr_px[
long_pos_mask]
if short_stocks != 0:
pos[short_pos_mask] = -SHORT_PCT * NET_BET * eq[
i - 1] / short_stocks / curr_px[
short_pos_mask]
else:
if long_stocks != 0:
pos[long_pos_mask] = LONG_PCT * NET_BET / long_stocks / curr_px[
long_pos_mask]
if short_stocks != 0:
pos[short_pos_mask] = -SHORT_PCT * NET_BET / short_stocks / curr_px[
short_pos_mask]
# prediction_sorted = np.sort(np.abs(prediction[pos_mask]))
# bound_idx = max(-SELECTION, -prediction_sorted.shape[0])
# bound = prediction_sorted[bound_idx]
# prediction_pos_mask = prediction >= bound
# prediction_pos_mask |= prediction <= -bound
# pos_mask &= prediction_pos_mask
#
# num_stks = np.sum(pos_mask)
# if RECAP:
# pos[pos_mask] = NET_BET * eq[i - 1] / num_stks / curr_px[pos_mask] * np.sign(
# prediction[pos_mask])
# else:
# pos[pos_mask] = NET_BET / num_stks / curr_px[pos_mask] * np.sign(prediction[pos_mask])
pos_px = curr_px
if PRINT_TRADES:
stk_idxs = np.nonzero(pos)[0]
for l in range(stk_idxs.shape[0]):
stk_idx = stk_idxs[l]
trade_ticker = tickers[stk_idx]
trade_px = curr_px[stk_idx]
trade_pos = pos[stk_idx]
trades.append({
'date': date,
'ticker': trade_ticker,
'pos': trade_pos,
'px': trade_px,
'action': 'open',
'cash flow': -trade_pos * trade_px,
'capital': 0
})
td_idx += 1
prev_px = curr_px
prev_pos_mask = pos_mask
urpl = calc_pl(pos, curr_px, pos_px, eval_config.SLIPPAGE)
es_urpl = calc_pl_simple(es_pos, es_close_px, es_pos_px,
ES_PRICE_STEP)
vx_urpl = calc_pl_simple(vx_pos, vx_close_px, vx_pos_px,
VX_PRICE_STEP)
nlv = cash + urpl + es_urpl + vx_urpl
eq[i] = nlv
ts[i] = get_date_timestamp(date)
if PRINT_WEEKLY_PL_CHANGES:
if trading_day:
track_close_pos, track_open_pos = track_strategy.decide(
date, next_trading_date)
if track_close_pos:
close_nlv = nlv
close_date = date
weekly_balance.append((close_nlv - open_nlv,
open_date, close_date))
if track_open_pos:
open_nlv = nlv
open_date = date
if PRINT_PERIODIC_STAT or PRINT_DECLINE_GS:
test_eq = eq[after_2007_mask]
test_ts = ts[after_2007_mask]
period_beg_idx = 0
period_start_nlv_idx = 0
stat = []
gs_ch = []
gs_dd = []
for i in range(test_ts.shape[0]):
date = date_from_timestamp(test_ts[i])
period_beg_date = date_from_timestamp(
test_ts[period_beg_idx])
period_start_nlv_date = date_from_timestamp(
test_ts[period_start_nlv_idx])
if BY_QUARTER:
def check_new_period(period_beg_date, date):
quarter_beg_date = (period_beg_date.month -
1) // 3
quarter_date = (date.month - 1) // 3
if quarter_beg_date != quarter_date:
return True
return False
else:
def check_new_period(period_beg_date, date):
return period_beg_date.month != date.month
if check_new_period(period_beg_date, date):
min_equity = INIT_NLV * np.min(
test_eq[period_start_nlv_idx:i])
nlv_beg = INIT_NLV * test_eq[period_start_nlv_idx]
nlv_end = INIT_NLV * test_eq[i - 1]
pct_ch = (nlv_end - nlv_beg) / nlv_beg
dd = (min_equity - nlv_beg) / nlv_beg
period_end_date = date_from_timestamp(test_ts[i -
1])
period_beg_idx = i
period_start_nlv_idx = i - 1
if PRINT_DECLINE_GS:
gs_ch.append(pct_ch)
gs_dd.append(dd)
if PRINT_PERIODIC_STAT:
stat.append({
"date beg": period_start_nlv_date,
"date end": period_end_date,
"nlv beg": nlv_beg,
"nlv end": nlv_end,
"dd": dd,
"change pct": pct_ch,
})
if PRINT_DECLINE_GS:
avg_gs_ch = np.mean(np.array(gs_ch))
avg_gs_dd = np.mean(np.array(gs_dd))
if PRINT_PERIODIC_STAT:
stat_df = pd.DataFrame(stat)
stat_df = stat_df[[
'date beg', 'date end', 'nlv beg', 'nlv end', 'dd',
'change pct'
]]
stat_df.to_csv(
"data/%s_%s_stat.csv" %
(linear_combination_normalizer,
"quaterly" if BY_QUARTER else "monthly"),
index=False)
# plot eq
folder_path, file_path = folders.get_adaptive_plot_path(
"DECLINE", decline_params, EPOCH)
folders.create_dir(folder_path)
fig = plots.plot_eq("total", eq[after_2007_mask],
ts[after_2007_mask])
fig.savefig(file_path)
# if fig is None:
# fig, ax = plots.create_eq_ax("equity curve")
#
# plots.plot_serie(ax, eq[after_2007_mask], ts[after_2007_mask])
if HIDE_PLOT:
plots.close_fig(fig)
test = False
train = False
test_idxs = np.nonzero(ts > CV_TS)[0]
if test_idxs.shape[0] > 0:
test = True
train_idxs = np.nonzero(ts <= CV_TS)[0]
if train_idxs.shape[0] > 0:
train = True
test_dd = 0
test_sharpe = 0
test_y_avg = 0
train_dd = 0
train_sharpe = 0
train_y_avg = 0
train = False
# save stat
if test:
test_dd, test_sharpe, test_y_avg = get_eq_params(
eq[test_idxs], ts[test_idxs], RECAP)
if PRINT_DECLINE_GS:
print(
"%s dd %.2f%% y_avg %.2f%% sharpe %.2f avg_m_dd %.2f%% avg_m_ret %.2f%%"
% (decline_params, test_dd * 100, test_y_avg * 100,
test_sharpe, avg_gs_dd * 100, avg_gs_ch * 100))
else:
print("%s Test dd: %.2f%% y avg: %.2f%% sharpe: %.2f" %
(linear_combination_normalizer, test_dd * 100,
test_y_avg * 100, test_sharpe))
if train:
train_dd, train_sharpe, train_y_avg = get_eq_params(
eq[train_idxs], ts[train_idxs], RECAP)
print("%s Train dd: %.2f%% y avg: %.2f%% sharpe: %.2f" %
(linear_combination_normalizer, train_dd * 100,
train_y_avg * 100, train_sharpe))
if PRINT_TRADES:
pd.DataFrame(trades).to_csv("data/trades.csv", index=False)
if PRINT_WEEKLY_PL_CHANGES:
components = [[*x] for x in zip(*weekly_balance)]
change = components[0]
open_dates = components[1]
close_dates = components[2]
pd.DataFrame({
CH_COL_NAME: change,
OPEN_DATE_COL_NAME: open_dates,
CLOSE_DATE_COL_NAME: close_dates
}).to_csv(weekly_balance_file_name, index=False)
if not HIDE_PLOT:
plots.show_plots()
_dow_mean = np.mean(_dow)
_moy = np.arange(1, 13)
_moy_stddev = np.sqrt(np.cov(_moy))
_moy_mean = np.mean(_moy)
def _calc_pct(new, old):
if old != 0:
return (new - old) / old
return 0
_v_pct = np.vectorize(_calc_pct)
_day_of_week_extractor = lambda ts: date_from_timestamp(ts).isoweekday()
_v_day_of_week_extractor = np.vectorize(_day_of_week_extractor)
_month_extractor = lambda ts: date_from_timestamp(ts).month
_v_month_extractor = np.vectorize(_month_extractor)
class DataSource(object):
def __init__(self, ticker):
self.ticker = ticker
print('loading data for %s...' % ticker)
dump_file_name = get_config().get_dump_file_name(self.ticker)
data = np.load(dump_file_name)