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Change config parameter names to improve clarity and consistency throughout the code (!!breaking change, please check discord support channel for migration instructions or review templates/FreqaiExampleStrategy.py config_examples/config_freqai_futures.example.json file changes!!)

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This commit is contained in:
Robert Caulk
2022-07-10 12:34:09 +02:00
parent 819cc9c0e4
commit 607455919e
10 changed files with 269 additions and 196 deletions
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64
freqtrade/freqai/data_kitchen.py
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@@ -26,6 +26,7 @@ from freqtrade.strategy.interface import IStrategy
SECONDS_IN_DAY = 86400
SECONDS_IN_HOUR = 3600
logger = logging.getLogger(__name__)
@@ -59,13 +60,13 @@ class FreqaiDataKitchen:
self.set_all_pairs()
if not self.live:
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period")
self.config["timerange"], self.freqai_config.get("train_period_days")
)
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
self.full_timerange,
config["freqai"]["train_period"],
config["freqai"]["backtest_period"],
config["freqai"]["train_period_days"],
config["freqai"]["backtest_period_days"],
)
# self.strat_dataframe: DataFrame = strat_dataframe
self.dd = data_drawer
@@ -234,17 +235,18 @@ class FreqaiDataKitchen:
:filtered_dataframe: cleaned dataframe ready to be split.
:labels: cleaned labels ready to be split.
"""
feat_dict = self.freqai_config.get("feature_parameters", {})
weights: npt.ArrayLike
if self.freqai_config["feature_parameters"].get("weight_factor", 0) > 0:
if feat_dict.get("weight_factor", 0) > 0:
weights = self.set_weights_higher_recent(len(filtered_dataframe))
else:
weights = np.ones(len(filtered_dataframe))
if self.freqai_config["feature_parameters"].get("stratify", 0) > 0:
if feat_dict.get("stratify_training_data", 0) > 0:
stratification = np.zeros(len(filtered_dataframe))
for i in range(1, len(stratification)):
if i % self.freqai_config.get("feature_parameters", {}).get("stratify", 0) == 0:
if i % feat_dict.get("stratify_training_data", 0) == 0:
stratification[i] = 1
else:
stratification = None
@@ -439,7 +441,7 @@ class FreqaiDataKitchen:
bt_split: the backtesting length (dats). Specified in user configuration file
"""
train_period = train_split * SECONDS_IN_DAY
train_period_days = train_split * SECONDS_IN_DAY
bt_period = bt_split * SECONDS_IN_DAY
full_timerange = TimeRange.parse_timerange(tr)
@@ -460,7 +462,7 @@ class FreqaiDataKitchen:
while True:
if not first:
timerange_train.startts = timerange_train.startts + bt_period
timerange_train.stopts = timerange_train.startts + train_period
timerange_train.stopts = timerange_train.startts + train_period_days
first = False
start = datetime.datetime.utcfromtimestamp(timerange_train.startts)
@@ -763,7 +765,7 @@ class FreqaiDataKitchen:
return
def create_fulltimerange(self, backtest_tr: str, backtest_period: int) -> str:
def create_fulltimerange(self, backtest_tr: str, backtest_period_days: int) -> str:
backtest_timerange = TimeRange.parse_timerange(backtest_tr)
if backtest_timerange.stopts == 0:
@@ -771,7 +773,8 @@ class FreqaiDataKitchen:
datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
)
backtest_timerange.startts = backtest_timerange.startts - backtest_period * SECONDS_IN_DAY
backtest_timerange.startts = (backtest_timerange.startts
- backtest_period_days * SECONDS_IN_DAY)
start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts)
stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts)
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
@@ -817,7 +820,8 @@ class FreqaiDataKitchen:
data_load_timerange = TimeRange()
# find the max indicator length required
max_timeframe_chars = self.freqai_config.get("timeframes")[-1]
max_timeframe_chars = self.freqai_config.get(
"feature_parameters", {}).get("include_timeframes")[-1]
max_period = self.freqai_config.get("feature_parameters", {}).get(
"indicator_max_period", 50
)
@@ -840,11 +844,11 @@ class FreqaiDataKitchen:
# logger.info(f'Extending data download by {additional_seconds/SECONDS_IN_DAY:.2f} days')
if trained_timestamp != 0:
elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY
retrain = elapsed_time > self.freqai_config.get("backtest_period")
elapsed_time = (time - trained_timestamp) / SECONDS_IN_HOUR
retrain = elapsed_time > self.freqai_config.get("live_retrain_hours", 0)
if retrain:
trained_timerange.startts = int(
time - self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
)
trained_timerange.stopts = int(time)
# we want to load/populate indicators on more data than we plan to train on so
@@ -852,19 +856,19 @@ class FreqaiDataKitchen:
# unless they have data further back in time before the start of the train period
data_load_timerange.startts = int(
time
- self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
- additional_seconds
)
data_load_timerange.stopts = int(time)
else: # user passed no live_trained_timerange in config
trained_timerange.startts = int(
time - self.freqai_config.get("train_period") * SECONDS_IN_DAY
time - self.freqai_config.get("train_period_days") * SECONDS_IN_DAY
)
trained_timerange.stopts = int(time)
data_load_timerange.startts = int(
time
- self.freqai_config.get("train_period", 0) * SECONDS_IN_DAY
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
- additional_seconds
)
data_load_timerange.stopts = int(time)
@@ -930,7 +934,7 @@ class FreqaiDataKitchen:
refresh_backtest_ohlcv_data(
exchange,
pairs=self.all_pairs,
timeframes=self.freqai_config.get("timeframes"),
timeframes=self.freqai_config.get("feature_parameters", {}).get("include_timeframes"),
datadir=self.config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
@@ -948,12 +952,12 @@ class FreqaiDataKitchen:
:params:
dataframe: DataFrame = strategy provided dataframe
"""
feat_params = self.freqai_config.get("feature_parameters", {})
with self.dd.history_lock:
history_data = self.dd.historic_data
for pair in self.all_pairs:
for tf in self.freqai_config.get("timeframes"):
for tf in feat_params.get("include_timeframes"):
# check if newest candle is already appended
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
@@ -992,7 +996,8 @@ class FreqaiDataKitchen:
def set_all_pairs(self) -> None:
self.all_pairs = copy.deepcopy(self.freqai_config.get("corr_pairlist", []))
self.all_pairs = copy.deepcopy(self.freqai_config.get(
'feature_parameters', {}).get('include_corr_pairlist', []))
for pair in self.config.get("exchange", "").get("pair_whitelist"):
if pair not in self.all_pairs:
self.all_pairs.append(pair)
@@ -1003,14 +1008,14 @@ class FreqaiDataKitchen:
Only called once upon startup of bot.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period
for training according to user defined train_period_days
"""
history_data = self.dd.historic_data
for pair in self.all_pairs:
if pair not in history_data:
history_data[pair] = {}
for tf in self.freqai_config.get("timeframes"):
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
history_data[pair][tf] = load_pair_history(
datadir=self.config["datadir"],
timeframe=tf,
@@ -1028,7 +1033,7 @@ class FreqaiDataKitchen:
to the present pair.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period
for training according to user defined train_period_days
metadata: dict = strategy furnished pair metadata
"""
@@ -1036,9 +1041,10 @@ class FreqaiDataKitchen:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}
historic_data = self.dd.historic_data
pairs = self.freqai_config.get("corr_pairlist", [])
pairs = self.freqai_config.get('feature_parameters', {}).get(
'include_corr_pairlist', [])
for tf in self.freqai_config.get("timeframes"):
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
base_dataframes[tf] = self.slice_dataframe(timerange, historic_data[pair][tf])
if pairs:
for p in pairs:
@@ -1057,7 +1063,7 @@ class FreqaiDataKitchen:
# DataFrame]:
# corr_dataframes: Dict[Any, Any] = {}
# base_dataframes: Dict[Any, Any] = {}
# pairs = self.freqai_config.get('corr_pairlist', []) # + [metadata['pair']]
# pairs = self.freqai_config.get('include_corr_pairlist', []) # + [metadata['pair']]
# # timerange = TimeRange.parse_timerange(new_timerange)
# for tf in self.freqai_config.get('timeframes'):
@@ -1101,9 +1107,9 @@ class FreqaiDataKitchen:
dataframe: DataFrame = dataframe containing populated indicators
"""
dataframe = base_dataframes[self.config["timeframe"]].copy()
pairs = self.freqai_config.get("corr_pairlist", [])
pairs = self.freqai_config.get('feature_parameters', {}).get('include_corr_pairlist', [])
sgi = True
for tf in self.freqai_config.get("timeframes"):
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
dataframe = strategy.populate_any_indicators(
pair,
pair,