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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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80
freqtrade/freqai/data_drawer.py
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@ -4,6 +4,7 @@ import logging
import re
import shutil
import threading
import warnings
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
@ -262,7 +263,8 @@ class FreqaiDataDrawer:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
return
def set_initial_return_values(self, pair: str,
def set_initial_return_values(
self, pair: str,
pred_df: DataFrame,
dataframe: DataFrame
) -> None:
@ -280,8 +282,13 @@ class FreqaiDataDrawer:
# 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
# append_model_predictions)
new_pred.iloc[:, :] = new_pred.astype(float).values * np.nan
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()
# 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
# 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:
new_pred = new_pred.iloc[len(common_dates):]
else:
@ -298,15 +306,27 @@ class FreqaiDataDrawer:
"predictions. You likely left your FreqAI instance offline "
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
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
# downtime in FreqUI
df_concat = df_concat.fillna(0)
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,
do_preds: NDArray[np.int_],
@ -323,38 +343,56 @@ class FreqaiDataDrawer:
index = self.historic_predictions[pair].index[-1:]
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], zeros_df], ignore_index=True, axis=0)
[self.historic_predictions[pair], zeros_df],
ignore_index=True,
axis=0
)
df = self.historic_predictions[pair]
# model outputs and associated statistics
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:
continue
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
label_mean_loc = df.columns.get_loc(f"{label}_mean")
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
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:
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
if dk.data['extra_returns_per_train']:
rets = dk.data['extra_returns_per_train']
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
# change their identifier
if 'close_price' not in df.columns:
df['close_price'] = np.nan
df['date_pred'] = np.nan
df['close_price'].iloc[-1] = strat_df['close'].iloc[-1]
df['date_pred'].iloc[-1] = strat_df['date'].iloc[-1]
high_price_loc = df.columns.get_loc("high_price")
high_loc = strat_df.columns.get_loc("high")
df.iloc[-1, high_price_loc] = strat_df.iloc[-1, high_loc]
low_price_loc = df.columns.get_loc("low_price")
low_loc = strat_df.columns.get_loc("low")
df.iloc[-1, low_price_loc] = strat_df.iloc[-1, low_loc]
close_price_loc = df.columns.get_loc("close_price")
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)