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fix: pandas.

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This commit is contained in:
robcaulk 2024-04-07 13:50:59 +02:00
parent 6a8abd5d45
commit 08d0e76187
1 changed files with 64 additions and 26 deletions
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freqtrade/freqai/data_drawer.py
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@ -4,6 +4,7 @@ import logging
import re import re
import shutil import shutil
import threading import threading
import warnings
from datetime import datetime, timedelta, timezone from datetime import datetime, timedelta, timezone
from pathlib import Path from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict from typing import Any, Dict, Tuple, TypedDict
@ -262,7 +263,8 @@ class FreqaiDataDrawer:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy() self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
return return
def set_initial_return_values(self, pair: str, def set_initial_return_values(
self, pair: str,
pred_df: DataFrame, pred_df: DataFrame,
dataframe: DataFrame dataframe: DataFrame
) -> None: ) -> None:
@ -280,8 +282,13 @@ class FreqaiDataDrawer:
# set new_pred values to nans (we want to signal to user that there was nothing # set new_pred values to nans (we want to signal to user that there was nothing
# historically made during downtime. The newest pred will get appeneded later in # historically made during downtime. The newest pred will get appeneded later in
# append_model_predictions) # append_model_predictions)
new_pred.iloc[:, :] = new_pred.astype(float).values * np.nan
new_pred["date_pred"] = dataframe["date"] new_pred["date_pred"] = dataframe["date"]
# set everything to nan except date_pred
columns_to_nan = new_pred.columns.difference(['date_pred'])
new_pred[columns_to_nan] = new_pred[columns_to_nan].astype(
float).values * np.nan
hist_preds = self.historic_predictions[pair].copy() hist_preds = self.historic_predictions[pair].copy()
# ensure both dataframes have the same date format so they can be merged # ensure both dataframes have the same date format so they can be merged
@ -290,7 +297,8 @@ class FreqaiDataDrawer:
# find the closest common date between new_pred and historic predictions # find the closest common date between new_pred and historic predictions
# and cut off the new_pred dataframe at that date # and cut off the new_pred dataframe at that date
common_dates = pd.merge(new_pred, hist_preds, on="date_pred", how="inner") common_dates = pd.merge(new_pred, hist_preds,
on="date_pred", how="inner")
if len(common_dates.index) > 0: if len(common_dates.index) > 0:
new_pred = new_pred.iloc[len(common_dates):] new_pred = new_pred.iloc[len(common_dates):]
else: else:
@ -298,15 +306,27 @@ class FreqaiDataDrawer:
"predictions. You likely left your FreqAI instance offline " "predictions. You likely left your FreqAI instance offline "
f"for more than {len(dataframe.index)} candles.") f"for more than {len(dataframe.index)} candles.")
if len(new_pred.index) == 0:
df_concat = hist_preds.tail(
len(dataframe.index)).reset_index(drop=True)
else:
# Pandas warns that its keeping dtypes of non NaN columns...
# yea we know and we already want that behavior. Ignoring.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
# reindex new_pred columns to match the historic predictions dataframe # reindex new_pred columns to match the historic predictions dataframe
new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns) new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
df_concat = pd.concat([hist_preds, new_pred_reindexed], ignore_index=True) df_concat = pd.concat(
[hist_preds, new_pred_reindexed],
ignore_index=True
)
# any missing values will get zeroed out so users can see the exact # any missing values will get zeroed out so users can see the exact
# downtime in FreqUI # downtime in FreqUI
df_concat = df_concat.fillna(0) df_concat = df_concat.fillna(0)
self.historic_predictions[pair] = df_concat self.historic_predictions[pair] = df_concat
self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True) self.model_return_values[pair] = df_concat.tail(
len(dataframe.index)).reset_index(drop=True)
def append_model_predictions(self, pair: str, predictions: DataFrame, def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_], do_preds: NDArray[np.int_],
@ -323,38 +343,56 @@ class FreqaiDataDrawer:
index = self.historic_predictions[pair].index[-1:] index = self.historic_predictions[pair].index[-1:]
columns = self.historic_predictions[pair].columns columns = self.historic_predictions[pair].columns
zeros_df = pd.DataFrame(np.zeros((1, len(columns))), index=index, columns=columns) zeros_df = pd.DataFrame(
np.zeros((1, len(columns))),
index=index,
columns=columns
)
self.historic_predictions[pair] = pd.concat( self.historic_predictions[pair] = pd.concat(
[self.historic_predictions[pair], zeros_df], ignore_index=True, axis=0) [self.historic_predictions[pair], zeros_df],
ignore_index=True,
axis=0
)
df = self.historic_predictions[pair] df = self.historic_predictions[pair]
# model outputs and associated statistics # model outputs and associated statistics
for label in predictions.columns: for label in predictions.columns:
df[label].iloc[-1] = predictions[label].iloc[-1] label_loc = df.columns.get_loc(label)
pred_label_loc = predictions.columns.get_loc(label)
df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
if df[label].dtype == object: if df[label].dtype == object:
continue continue
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label] label_mean_loc = df.columns.get_loc(f"{label}_mean")
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label] label_std_loc = df.columns.get_loc(f"{label}_std")
df.iloc[-1, label_mean_loc] = dk.data["labels_mean"][label]
df.iloc[-1, label_std_loc] = dk.data["labels_std"][label]
# outlier indicators # outlier indicators
df["do_predict"].iloc[-1] = do_preds[-1] do_predict_loc = df.columns.get_loc("do_predict")
df.iloc[-1, do_predict_loc] = do_preds[-1]
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0: if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
df["DI_values"].iloc[-1] = dk.DI_values[-1] DI_values_loc = df.columns.get_loc("DI_values")
df.iloc[-1, DI_values_loc] = dk.DI_values[-1]
# extra values the user added within custom prediction model # extra values the user added within custom prediction model
if dk.data['extra_returns_per_train']: if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train'] rets = dk.data['extra_returns_per_train']
for return_str in rets: for return_str in rets:
df[return_str].iloc[-1] = rets[return_str] return_loc = df.columns.get_loc(return_str)
df.iloc[-1, return_loc] = rets[return_str]
# this logic carries users between version without needing to high_price_loc = df.columns.get_loc("high_price")
# change their identifier high_loc = strat_df.columns.get_loc("high")
if 'close_price' not in df.columns: df.iloc[-1, high_price_loc] = strat_df.iloc[-1, high_loc]
df['close_price'] = np.nan low_price_loc = df.columns.get_loc("low_price")
df['date_pred'] = np.nan low_loc = strat_df.columns.get_loc("low")
df.iloc[-1, low_price_loc] = strat_df.iloc[-1, low_loc]
df['close_price'].iloc[-1] = strat_df['close'].iloc[-1] close_price_loc = df.columns.get_loc("close_price")
df['date_pred'].iloc[-1] = strat_df['date'].iloc[-1] close_loc = strat_df.columns.get_loc("close")
df.iloc[-1, close_price_loc] = strat_df.iloc[-1, close_loc]
date_pred_loc = df.columns.get_loc("date_pred")
date_loc = strat_df.columns.get_loc("date")
df.iloc[-1, date_pred_loc] = strat_df.iloc[-1, date_loc]
self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True) self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)