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ruff format: freqai

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Matthias
2024-05-12 17:12:20 +02:00
parent e4e8c3967c
commit d1db43dee0
44 changed files with 1111 additions and 900 deletions
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81
freqtrade/freqai/utils.py
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@@ -31,11 +31,12 @@ def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
"""
if dp._exchange is None:
raise OperationalException('No exchange object found.')
raise OperationalException("No exchange object found.")
markets = [
p for p in dp._exchange.get_markets(
tradable_only=True, active_only=not config.get('include_inactive')
).keys()
p
for p in dp._exchange.get_markets(
tradable_only=True, active_only=not config.get("include_inactive")
).keys()
]
all_pairs = dynamic_expand_pairlist(config, markets)
@@ -73,42 +74,39 @@ def get_required_data_timerange(config: Config) -> TimeRange:
if secs > max_tf_seconds:
max_tf_seconds = secs
startup_candles = config.get('startup_candle_count', 0)
startup_candles = config.get("startup_candle_count", 0)
indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
# factor the max_period as a factor of safety.
max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
config['startup_candle_count'] = max_period
logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
config["startup_candle_count"] = max_period
logger.info(f"FreqAI auto-downloader using {max_period} startup candles.")
additional_seconds = max_period * max_tf_seconds
startts = int(
time
- config["freqai"].get("train_period_days", 0) * 86400
- additional_seconds
)
startts = int(time - config["freqai"].get("train_period_days", 0) * 86400 - additional_seconds)
stopts = int(time)
data_load_timerange = TimeRange('date', 'date', startts, stopts)
data_load_timerange = TimeRange("date", "date", startts, stopts)
return data_load_timerange
def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
count_max: int = 25) -> None:
def plot_feature_importance(
model: Any, pair: str, dk: FreqaiDataKitchen, count_max: int = 25
) -> None:
"""
Plot Best and worst features by importance for a single sub-train.
:param model: Any = A model which was `fit` using a common library
such as catboost or lightgbm
:param pair: str = pair e.g. BTC/USD
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param count_max: int = the amount of features to be loaded per column
Plot Best and worst features by importance for a single sub-train.
:param model: Any = A model which was `fit` using a common library
such as catboost or lightgbm
:param pair: str = pair e.g. BTC/USD
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param count_max: int = the amount of features to be loaded per column
"""
from freqtrade.plot.plotting import go, make_subplots, store_plot_file
# Extract feature importance from model
models = {}
if 'FreqaiMultiOutputRegressor' in str(model.__class__):
if "FreqaiMultiOutputRegressor" in str(model.__class__):
for estimator, label in zip(model.estimators_, dk.label_list):
models[label] = estimator
else:
@@ -123,14 +121,16 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
elif "xgb" in str(mdl.__class__):
feature_importance = mdl.feature_importances_
else:
logger.info('Model type does not support generating feature importances.')
logger.info("Model type does not support generating feature importances.")
return
# Data preparation
fi_df = pd.DataFrame({
"feature_names": np.array(dk.data_dictionary['train_features'].columns),
"feature_importance": np.array(feature_importance)
})
fi_df = pd.DataFrame(
{
"feature_names": np.array(dk.data_dictionary["train_features"].columns),
"feature_importance": np.array(feature_importance),
}
)
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
@@ -140,14 +140,18 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
go.Bar(
x=fi_df["feature_importance"],
y=fi_df["feature_names"],
orientation='h', showlegend=False
), row=1, col=col
orientation="h",
showlegend=False,
),
row=1,
col=col,
)
fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
fig = add_feature_trace(fig, fi_df_top, 1)
fig = add_feature_trace(fig, fi_df_worst, 2)
fig.update_layout(title_text=f"Best and worst features by importance {pair}")
label = label.replace('&', '').replace('%', '') # escape two FreqAI specific characters
label = label.replace("&", "").replace("%", "") # escape two FreqAI specific characters
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
@@ -158,12 +162,12 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
params_record_path = full_path / "run_params.json"
run_params = {
"freqai": config.get('freqai', {}),
"timeframe": config.get('timeframe'),
"stake_amount": config.get('stake_amount'),
"stake_currency": config.get('stake_currency'),
"max_open_trades": config.get('max_open_trades'),
"pairs": config.get('exchange', {}).get('pair_whitelist')
"freqai": config.get("freqai", {}),
"timeframe": config.get("timeframe"),
"stake_amount": config.get("stake_amount"),
"stake_currency": config.get("stake_currency"),
"max_open_trades": config.get("max_open_trades"),
"pairs": config.get("exchange", {}).get("pair_whitelist"),
}
with params_record_path.open("w") as handle:
@@ -172,7 +176,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
handle,
indent=4,
default=str,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
)
@@ -191,10 +195,11 @@ def get_timerange_backtest_live_models(config: Config) -> str:
def get_tb_logger(model_type: str, path: Path, activate: bool) -> Any:
if model_type == "pytorch" and activate:
from freqtrade.freqai.tensorboard import TBLogger
return TBLogger(path, activate)
else:
from freqtrade.freqai.tensorboard.base_tensorboard import BaseTensorboardLogger
return BaseTensorboardLogger(path, activate)