for c in config:
results.append(strategy_long(data, **c))
#align and join results
results = xr.align(*results, join='outer')
results = [r.fillna(0) for r in results]
output = sum(results) / len(results)
return output
config = json.load(open('config.json', 'r'))
# single-pass
data = qndata.futures_load_data(min_date='2005-01-01')
output = bag_strategy(data, config)
output = qnout.clean(output, data)
stats = qnstats.calc_stat(data, output.sel(time=slice('2006-01-01', None)))
print(stats.to_pandas().tail())
# qngraph.make_major_plots(stats) # works in juoyter
qnout.check(output, data)
qnout.write(output)
# # multi-pass
# # It may look slow, but it is ok. The evaluator will run only one iteration per day.
# qnbk.backtest(
# competition_type='futures',
# lookback_period=365,
# strategy=lambda d: bag_strategy(d, config),
# # strategy=strategy_long,
def strategy(data):
# calc weights:
close = data.sel(field="close")
ma_slow = qnta.lwma(close, 50)
ma_fast = qnta.lwma(close, 10)
return xr.where(ma_fast > ma_slow, 1, -1)
# SINGLE-PASS
# ---
# This is fast implementation, but it can easily become looking forward (common problem).
# Use this approach for research and optimization. And use multi-pass to detect looking forward.
data = qndata.cryptofutures.load_data(min_date="2013-04-01") # load data
output = strategy(data)
output = qnout.clean(output, data) # fix common errors
qnout.check(output, data) # check that weights are correct:
qnout.write(output) # write results, necessary for submission:
stats = qnstats.calc_stat(data, output.sel(time=slice("2014-01-01", None))) # calc stats
print(stats.to_pandas().tail())
# qngraph.make_major_plots(stats) # works in jupyter
# ---
# # # MULTI-PASS
# # # ---
# # Use this approach to make sure that your strategy is not looking forward.
# weights = qnbt.backtest(
# competition_type="cryptofutures", # BTC Futures contest
def backtest(*,
competition_type: str,
strategy: tp.Union[
tp.Callable[[DataSet], xr.DataArray],
tp.Callable[[DataSet, tp.Any], tp.Tuple[xr.DataArray, tp.Any]],
],
load_data: tp.Union[tp.Callable[[int], tp.Union[DataSet,tp.Tuple[DataSet,np.ndarray]]],None] = None,
lookback_period: int = 365,
test_period: int = 365*15,
start_date: tp.Union[np.datetime64, str, datetime.datetime, datetime.date, None] = None,
window: tp.Union[tp.Callable[[DataSet,np.datetime64,int], DataSet], None] = None,
step: int = 1,
analyze: bool = True,
build_plots: bool = True,
collect_all_states: bool = False,
):
"""
:param competition_type: "futures" | "stocks" | "cryptofutures" | "stocks_long" | "crypto"
:param load_data: data load function, accepts tail arg, returns time series and data
:param lookback_period: calendar days period for one iteration
:param strategy: accepts data, returns weights distribution for the last day
:param test_period: test period (calendar days)
:param start_date: start date for backtesting, overrides test period
:param step: step size
:param window: function which isolates data for one iterations
:param analyze: analyze the output and calc stats
:param build_plots: build plots (require analyze=True)
:patam collect_all_states: collect all states instead of the last one
:return:
"""
qndc.track_event("BACKTEST")
if window is None:
window = standard_window
if load_data is None:
load_data = lambda tail: qndata.load_data_by_type(competition_type, tail=tail)
args_count = len(inspect.getfullargspec(strategy).args)
strategy_wrap = (lambda d, s: strategy(d)) if args_count < 2 else strategy
log_info("Run last pass...")
log_info("Load data...")
data = load_data(lookback_period)
try:
if data.name == 'stocks' and competition_type != 'stocks' and competition_type != 'stocks_long'\
or data.name == 'cryptofutures' and competition_type != 'cryptofutures' and competition_type != 'crypto_futures'\
or data.name == 'crypto' and competition_type != 'crypto'\
or data.name == 'futures' and competition_type != 'futures':
log_err("WARNING! The data type and the competition type are mismatch.")
except:
pass
data, time_series = extract_time_series(data)
log_info("Run strategy...")
state = None
if is_submitted() and args_count > 1:
state = qnstate.read()
result = strategy_wrap(data, state)
result, state = unpack_result(result)
log_info("Load data for cleanup...")
data = qndata.load_data_by_type(competition_type, assets=result.asset.values.tolist(), tail=60)
result = qnout.clean(result, data)
result.name = competition_type
log_info("Write result...")
qnout.write(result)
qnstate.write(state)
if is_submitted():
if args_count > 1:
return result, state
else:
return result
log_info("---")
if start_date is None:
start_date = pd.Timestamp.today().to_datetime64() - np.timedelta64(test_period-1, 'D')
else:
start_date = pd.Timestamp(start_date).to_datetime64()
test_period = (pd.Timestamp.today().to_datetime64() - start_date) / np.timedelta64(1, 'D')
log_info("Run first pass...")
try:
qndc.MAX_DATETIME_LIMIT = pd.Timestamp(start_date).to_pydatetime()
qndc.MAX_DATE_LIMIT = qndc.MAX_DATETIME_LIMIT.date()
print("Load data...")
data = load_data(lookback_period)
data, time_series = extract_time_series(data)
print("Run strategy...")
result = strategy_wrap(data, None)
result, state = unpack_result(result)
finally:
qndc.MAX_DATE_LIMIT = None
qndc.MAX_DATETIME_LIMIT = None
log_info("---")
log_info("Load full data...")
data = load_data(test_period + lookback_period)
data, time_series = extract_time_series(data)
if len(time_series) < 1:
log_err("Time series is empty")
return
log_info("---")
result, state = run_iterations(time_series, data, window, start_date, lookback_period, strategy_wrap, step, collect_all_states)
if result is None:
return
log_info("Load data for cleanup and analysis...")
min_date = time_series[0] - np.timedelta64(60, 'D')
data = qndata.load_data_by_type(competition_type, assets=result.asset.values.tolist(), min_date=str(min_date)[:10])
result = qnout.clean(result, data, competition_type)
result.name = competition_type
log_info("Write result...")
qnout.write(result)
qnstate.write(state)
if analyze:
log_info("---")
analyze_results(result, data, competition_type, build_plots)
if args_count > 1:
return result, state
else:
return result
# calc weights:
close = data.sel(field="close")
is_liquid = data.sel(field='is_liquid')
ma_slow = qnta.lwma(close, 50)
ma_fast = qnta.lwma(close, 10)
return xr.where(ma_fast > ma_slow, 1, 0) * is_liquid
# SINGLE-PASS
# ---
# This is fast implementation, but it can easily become looking forward (common problem).
# Use this approach for research and optimization. And use multi-pass to detect looking forward.
data = qndata.cryptodaily.load_data(min_date="2013-04-01") # load data
output = strategy(data)
output = qnout.clean(output, data, 'crypto_daily_long') # fix common errors
qnout.check(output, data, 'crypto_daily_long') # check that weights are correct:
qnout.write(output) # write results, necessary for submission:
stats = qnstats.calc_stat(data, output.sel(time=slice("2014-01-01", None))) # calc stats
print(stats.to_pandas().tail())
# qngraph.make_major_plots(stats) # works in jupyter
# ---
# # MULTI-PASS
# # ---
# Use this approach to make sure that your strategy is not looking forward.
# weights = qnbt.backtest(
# competition_type='crypto_daily_long', # Crypto Daily Long contest
def backtest_ml(
*,
train: tp.Callable[[DataSet], tp.Any],
predict: tp.Union[tp.Callable[[tp.Any, DataSet], xr.DataArray],
tp.Callable[[tp.Any, DataSet, tp.Any],
tp.Tuple[xr.DataArray, tp.Any]], ],
train_period: int = 4 * 365,
retrain_interval: int = 365,
predict_each_day: bool = False,
retrain_interval_after_submit: tp.Union[int, None] = None,
competition_type: str,
load_data: tp.Union[tp.Callable[[int], tp.Union[DataSet,
tp.Tuple[DataSet,
np.ndarray]]],
None] = None,
lookback_period: int = 365,
test_period: int = 365 * 15,
start_date: tp.Union[np.datetime64, str, datetime.datetime, datetime.date,
None] = None,
end_date: tp.Union[np.datetime64, str, datetime.datetime, datetime.date,
None] = None,
window: tp.Union[tp.Callable[[DataSet, np.datetime64, int], DataSet],
None] = None,
analyze: bool = True,
build_plots: bool = True,
collect_all_states: bool = False,
):
"""
:param train: creates and trains model for prediction
:param predict: predicts price movements and generates outputs
:param train_period: the data length in trading days for training
:param retrain_interval: how often to retrain the model(in calendar days)
:param predict_each_day: perform predict for every day. Set True if you suspect the looking forward
:param retrain_interval_after_submit:
:param competition_type: "futures" | "stocks" | "cryptofutures" | "stocks_long" | "crypto" | "crypto_daily"
:param load_data: data load function, accepts tail arg, returns time series and data
:param lookback_period: the minimal calendar days period for one prediction
:param test_period: test period (calendar days)
:param start_date: start date for backtesting, overrides test period
:param end_date: end date for backtesting, by default - now
:param window: function which isolates data for one prediction or training
:param analyze: analyze the output and calc stats
:param build_plots: build plots (require analyze=True)
:param collect_all_states: collect all states instead of the last one
:return:
"""
qndc.track_event("ML_BACKTEST")
if load_data is None:
load_data = lambda tail: qndata.load_data_by_type(competition_type,
tail=tail)
if window is None:
window = standard_window
def copy_window(data, dt, tail):
return copy.deepcopy(window(data, dt, tail))
args_count = len(inspect.getfullargspec(predict).args)
predict_wrap = (
lambda m, d, s: predict(m, d)) if args_count < 3 else predict
log_info("Run the last iteration...")
data = load_data(max(train_period, lookback_period))
data, data_ts = extract_time_series(data)
retrain_interval_cur = retrain_interval_after_submit if is_submitted(
) else retrain_interval
if retrain_interval_cur is None:
retrain_interval_cur = retrain_interval
created = None
model = None
state = None
if is_submitted() and (args_count > 2 or retrain_interval_cur > 1):
state = qnstate.read()
if state is not None:
created = state[0]
model = state[1]
state = state[2]
need_retrain = model is None or retrain_interval_cur == 1 \
or data_ts[-1] >= created + np.timedelta64(retrain_interval_cur, 'D')
if need_retrain:
train_data_slice = copy_window(data, data_ts[-1], train_period)
model = train(train_data_slice)
created = data_ts[-1]
test_data_slice = copy_window(data, data_ts[-1], lookback_period)
output = predict_wrap(model, test_data_slice, state)
output, state = unpack_result(output)
if data_ts[-1] in output.time:
result = output.sel(time=[data_ts[-1]])
data = qndata.load_data_by_type(competition_type,
assets=result.asset.values.tolist(),
tail=60)
result = qnout.clean(result, data, competition_type)
result.name = competition_type
qnout.write(result)
if need_retrain and retrain_interval_cur > 1 or state is not None:
qnstate.write((created, model, state))
if is_submitted():
if state is not None:
return output, [state] if collect_all_states else state
else:
return output
try:
print("---")
qndc.set_max_datetime(end_date)
last_date = np.datetime64(qndc.parse_date(datetime.date.today()))
if start_date is None:
start_date = last_date - np.timedelta64(test_period - 1, 'D')
else:
start_date = pd.Timestamp(start_date).to_datetime64()
test_period = (last_date - start_date) // np.timedelta64(1, 'D')
# ---
log_info("Run First Iteration...") # to catch most errors
qndc.set_max_datetime(start_date)
data = load_data(max(train_period, lookback_period))
data, data_ts = extract_time_series(data)
train_data_slice = copy_window(data, data_ts[-1], train_period)
model = train(train_data_slice)
test_data_slice = copy_window(data, data_ts[-1], lookback_period)
output = predict_wrap(model, test_data_slice, state)
output, state = unpack_result(output)
# ---
print("---")
qndc.set_max_datetime(end_date)
log_info("Run all iterations...")
log_info('Load data...')
train_data = load_data(test_period + train_period + lookback_period)
train_data, train_ts = extract_time_series(train_data)
test_data = load_data(test_period)
test_ts = extract_time_series(test_data)[1]
log_info('Backtest...')
outputs = []
t = test_ts[0]
state = None
model = None
states = []
with progressbar.ProgressBar(max_value=len(test_ts),
poll_interval=1) as p:
go = True
while go:
end_t = t + np.timedelta64(max(retrain_interval - 1, 0), 'D')
end_t = test_ts[test_ts <= end_t][-1]
train_data_slice = copy_window(train_data, t, train_period)
# print("train model t <=", str(t)[:10])
model = train(train_data_slice)
# print("predict", str(t)[:10], "<= t <=", str(end_t)[:10])
if predict_each_day:
for test_t in test_ts[np.logical_and(
test_ts >= t, test_ts <= end_t)]:
test_data_slice = copy_window(train_data, test_t,
lookback_period)
output = predict_wrap(model, test_data_slice, state)
output, state = unpack_result(output)
if collect_all_states:
states.append(state)
if test_t in output.time:
output = output.sel(time=[test_t])
outputs.append(output)
p.update(np.where(test_ts == test_t)[0].item())
else:
test_data_slice = copy_window(
train_data, end_t, lookback_period + retrain_interval)
output = predict_wrap(model, test_data_slice, state)
output, state = unpack_result(output)
if collect_all_states:
states.append(state)
output = output.where(output.time >= t).where(
output.time <= end_t).dropna('time', 'all')
outputs.append(output)
p.update(np.where(test_ts == end_t)[0].item())
next_t = test_ts[test_ts > end_t]
if len(next_t) > 0:
t = next_t[0]
else:
go = False
result = xr.concat(outputs, dim='time')
min_date = test_ts[0] - np.timedelta64(60, 'D')
data = qndata.load_data_by_type(competition_type,
min_date=str(min_date)[:10])
result = qnout.clean(result, data, competition_type)
result.name = competition_type
qnout.write(result)
qnstate.write((t, model, state))
if analyze:
log_info("---")
analyze_results(result, data, competition_type, build_plots,
start_date)
if state is None:
return result
elif collect_all_states:
return result, states
else:
return result, state
finally:
qndc.set_max_datetime(None)