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feat(Events): added EventFilter, EventLabeller (#186)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-26 23:22:43 +01:00
committed by GitHub
parent 1042c82333
commit 42a1bc59cb
65 changed files with 759 additions and 276571 deletions
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utils/evaluate.py
+9 -9
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@@ -12,23 +12,23 @@ def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.002) ->
return (signal * returns) - costs
def __preprocess(target_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
def __preprocess(forward_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
y_pred.name = 'y_pred'
target_returns.name = 'target_returns'
df = pd.concat([y_pred, target_returns],axis=1).dropna()
forward_returns.name = 'forward_returns'
df = pd.concat([y_pred, forward_returns],axis=1).dropna()
discretize_func = get_discretize_function(no_of_classes)
# make sure that we evaluate binary/three-way predictions even if the model is a regression
df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
df['sign_true'] = y_true
df['result'] = backtest(df.target_returns, df.sign_pred)
df['result'] = backtest(df.forward_returns, df.sign_pred)
return df
def evaluate_predictions(
model_name: str,
target_returns: pd.Series,
forward_returns: pd.Series,
y_pred: pd.Series,
y_true: pd.Series,
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
@@ -36,12 +36,12 @@ def evaluate_predictions(
discretize: bool = False,
) -> pd.Series:
# ignore the predictions until we see a non-zero returns (and definitely skip the first sliding_window_size)
evaluate_from = max(get_first_valid_return_index(target_returns), get_first_valid_return_index(y_pred))
evaluate_from = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(y_pred))
target_returns = pd.Series(target_returns[evaluate_from:])
forward_returns = pd.Series(forward_returns[evaluate_from:])
y_pred = pd.Series(y_pred[evaluate_from:])
df = __preprocess(target_returns, y_pred, y_true, no_of_classes, discretize)
df = __preprocess(forward_returns, y_pred, y_true, no_of_classes, discretize)
scorecard = pd.Series()
@@ -51,7 +51,7 @@ def evaluate_predictions(
scorecard.loc['no_of_samples'] = no_of_samples
sharpe = sharpe_ratio(df.result)
scorecard.loc['sharpe'] = sharpe
benchmark_sharpe = sharpe_ratio(df.target_returns)
benchmark_sharpe = sharpe_ratio(df.forward_returns)
scorecard.loc['benchmark_sharpe'] = benchmark_sharpe
scorecard.loc['prob_sharpe'] = probabilistic_sharpe_ratio(sharpe, benchmark_sharpe, no_of_samples)
scorecard.loc['sortino'] = sortino(df.result)