mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
7aedb91069
* feat(Eval): added format_data_for_backtest() * feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO * feat(Core): added walk forward method of training/testing * fix(Model): remove the unnecessary softmax activation from the keras models * feat(Core): added walk_forward_train_test()
143 lines
5.5 KiB
Python
143 lines
5.5 KiB
Python
#%%
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import pandas as pd
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import os
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import numpy as np
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from pandas.core.frame import DataFrame
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from utils.technical_indicators import ROC, RSI, STOK, STOD
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from typing import Literal
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#%%
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def load_files(path: str,
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own_asset: str,
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load_other_assets: bool,
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log_returns: bool,
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add_date_features: bool,
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own_technical_features: Literal['none', 'level1', 'level2'],
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other_technical_features: Literal['none', 'level1', 'level2'],
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exogenous_features: Literal['none', 'level1'],
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index_column: Literal['date', 'int'],
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narrow_format: bool = False
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) -> pd.DataFrame:
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files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]
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files = [f for f in files if load_other_assets == True or (load_other_assets == False and f.startswith(own_asset))]
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def is_own_asset(own_asset: str, file: str): return file.split('.')[0].startswith(own_asset)
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dfs = [__load_df(path=os.path.join(path,f), prefix=f.split('.')[0], log_returns=log_returns, technical_features=own_technical_features if is_own_asset(own_asset, f) else other_technical_features, narrow_format=narrow_format) for f in files]
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if narrow_format:
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dfs = pd.concat(dfs, axis=0).fillna(0.)
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else:
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dfs = pd.concat(dfs, axis=1).fillna(0.)
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dfs.index = pd.DatetimeIndex(dfs.index)
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if add_date_features:
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dfs['day_month'] = dfs.index.day
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dfs['day_week'] = dfs.index.dayofweek
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dfs['month'] = dfs.index.month
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if index_column == 'int':
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dfs.reset_index(drop=True, inplace=True)
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if narrow_format:
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return dfs
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else:
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return dfs.drop(index=dfs.index[0], axis=0)
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def __load_df(path: str, prefix: str, log_returns: bool, technical_features: Literal['none', 'level1', 'level2'], narrow_format: bool = False) -> pd.DataFrame:
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df = pd.read_csv(path, header=0, index_col=0).fillna(0)
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if log_returns:
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df['returns'] = np.log(df['close']).diff(1)
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else:
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df['returns'] = df['close'].pct_change()
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df = __augment_derived_features(df, log_returns=log_returns, technical_features=technical_features)
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df = df.replace([np.inf, -np.inf], 0.)
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df = df.drop(columns=['open', 'high', 'low', 'close'])
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# we're not ready for this just yet
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if 'volume' in df.columns:
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df = df.drop(columns=['volume'])
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if narrow_format:
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df["ticker"] = np.repeat(prefix, df.shape[0])
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else:
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df.columns = [prefix + "_" + c for c in df.columns]
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return df
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def __augment_derived_features(df: pd.DataFrame, log_returns: bool, technical_features: Literal['none', 'level1', 'level2']) -> pd.DataFrame:
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if technical_features == 'level1' or technical_features == 'level2':
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# volatility (10, 20, 30 days)
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df['vol_10'] = df['returns'].rolling(10).std()*(252**0.5)
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df['vol_20'] = df['returns'].rolling(20).std()*(252**0.5)
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df['vol_30'] = df['returns'].rolling(30).std()*(252**0.5)
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df['vol_60'] = df['returns'].rolling(30).std()*(252**0.5)
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# momentum (10, 20, 30, 60, 90 days)
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if log_returns:
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df['mom_10'] = np.log(df['close']).diff(10)
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df['mom_20'] = np.log(df['close']).diff(20)
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df['mom_30'] = np.log(df['close']).diff(30)
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df['mom_60'] = np.log(df['close']).diff(60)
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df['mom_90'] = np.log(df['close']).diff(90)
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else:
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df['mom_10'] = df['close'].pct_change(10)
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df['mom_20'] = df['close'].pct_change(20)
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df['mom_30'] = df['close'].pct_change(30)
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df['mom_60'] = df['close'].pct_change(60)
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df['mom_90'] = df['close'].pct_change(90)
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if technical_features == 'level2':
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df['roc_10'] = ROC(df['close'], 10)
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df['roc_30'] = ROC(df['close'], 30)
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df['rsi_10'] = RSI(df['close'], 10)
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df['rsi_30'] = RSI(df['close'], 30)
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df['rsi_100'] = RSI(df['close'], 30)
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df['stok_10'] = STOK(df['close'], df['low'], df['high'], 10)
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df['stod_10'] = STOD(df['close'], df['low'], df['high'], 10)
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df['stok_30'] = STOK(df['close'], df['low'], df['high'], 30)
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df['stod_30'] = STOD(df['close'], df['low'], df['high'], 30)
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df['stok_200'] = STOK(df['close'], df['low'], df['high'], 200)
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df['stod_200'] = STOD(df['close'], df['low'], df['high'], 200)
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return df
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# %%
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def create_target_cum_forward_returns(df: pd.DataFrame, source_column: str, period: int) -> pd.DataFrame:
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df['target'] = df[source_column].diff(period).shift(-period)
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df = df.iloc[:-period]
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return df
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def create_target_classes(df: pd.DataFrame, source_column: str, period: int, no_of_classes: Literal["two", "three"]) -> pd.DataFrame:
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def get_class_binary(x):
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return 0 if x <= 0.0 else 1
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def get_class_threeway(x):
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bins = pd.qcut(df[source_column], 4, duplicates='raise', retbins=True)[1]
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lower_threshold = bins[1]
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upper_threshold = bins[3]
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if x <= lower_threshold:
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return -1
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elif x > lower_threshold and x < upper_threshold:
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return 0
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else:
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return 1
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if period > 0:
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df['target'] = df[source_column].shift(-period)
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else:
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df['target'] = df[source_column]
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get_class_function = get_class_binary
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if no_of_classes == "three":
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get_class_function = get_class_threeway
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df['target'] = df['target'].map(get_class_function)
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if period > 0:
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df = df.iloc[:-period]
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return df |