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refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)

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This commit is contained in:
Mark Aron Szulyovszky
2021-12-17 17:41:50 +01:00
committed by GitHub
parent cc7061b456
commit 0963df2087
94 changed files with 9704 additions and 84787 deletions
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utils/evaluate.py
+4 -6
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@@ -5,7 +5,7 @@ from utils.helpers import get_first_valid_return_index
import pandas as pd
import numpy as np
def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.01) -> pd.Series:
def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.00) -> pd.Series:
delta_pos = signal.diff(1).abs().fillna(0.)
costs = transaction_cost * delta_pos
return (signal * returns) - costs
@@ -36,20 +36,18 @@ def evaluate_predictions(
model_name: str,
target_returns: pd.Series,
y_pred: pd.Series,
sliding_window_size: int,
method: Literal['classification', 'regression']
) -> pd.Series:
# ignore the predictions until we see a non-zero returns (and definitely skip the first sliding_window_size)
first_nonzero_return = get_first_valid_return_index(target_returns)
evaluate_from = first_nonzero_return + sliding_window_size + 1
first_nonzero_return = max(get_first_valid_return_index(target_returns), get_first_valid_return_index(y_pred))
evaluate_from = first_nonzero_return + 1
target_returns = pd.Series(target_returns[evaluate_from:])
if method == 'regression':
# if there are lots of zeros in the ground truth returns, probably something is wrong, but we can tolerate a couple of days of missing data.
is_zero = target_returns[target_returns == 0]
assert len(is_zero) < 15
# we can't deal with 0 returns, so we'll just remap the few examples to 0.0001
target_returns = target_returns.apply(lambda x: 0.0001 if x == 0 else x)
y_pred = pd.Series(y_pred[evaluate_from:])
df = __preprocess(target_returns, y_pred, method)