signalseval

numereval/__init__.py

@@ -1,2 +1,3 @@
 from numereval.numereval import evaluate, diagnostics
-#from numereval.signalseval import *
+from numereval.signalseval import run_analytics
+from numereval.scores import score

numereval/scores.py

@@ -4,6 +4,7 @@
 TOURNAMENT_NAME = "nomi"
 TARGET_NAME = f"target"
 PREDICTION_NAME = f"prediction"
+PREDICTION_NAME_S = "signal"
 
 
 def correlation(predictions, targets):
@@ -16,6 +17,10 @@ def score(df):
     return correlation(df[PREDICTION_NAME], df[TARGET_NAME])
 
 
+def score_signals(df):
+    return correlation(df[PREDICTION_NAME_S], df[TARGET_NAME])
+
+
 # Payout is just the score cliped at +/-25%
 def payout(scores):
     return scores.clip(lower=-0.25, upper=0.25)

numereval/signalseval.py

@@ -0,0 +1,49 @@
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from numereval.scores import score_signals
+
+
+def calculate_max_drawdown(validation_correlations: pd.Series):
+
+    rolling_max = (
+        (validation_correlations + 1).cumprod().rolling(window=100, min_periods=1).max()
+    )
+    daily_value = (validation_correlations + 1).cumprod()
+    max_drawdown = -((rolling_max - daily_value)).max()
+
+    #TODO: Add Max drawdown weeks
+    return max_drawdown
+
+def run_analytics(era_scores, roll_mean=20, plot=False):
+
+    '''Calculates some stats and plot cumulative scores.
+        Taken from Jason Rosenfeld's notebook.
+    '''
+
+    metrics = {}
+
+    metrics["weeks"] = len(era_scores)
+    metrics["Mean correlation"] = era_scores.mean()
+    metrics["Median correlation"] = era_scores.median()
+    metrics["Std. Dev."] = era_scores.std()
+    metrics["Mean Pseudo-Sharpe"] = era_scores.mean()/era_scores.std()
+    metrics["Median Pseudo-Sharpe"] = era_scores.median()/era_scores.std()
+    metrics["Hit Rate (% positive eras)"] = era_scores.apply(lambda x: np.sign(x)).value_counts()[1]/len(era_scores) * 100
+    #metrics["Max Drawdown"] = calculate_max_drawdown(era_scores)
+
+    if plot:
+
+        era_scores.rolling(roll_mean).mean().plot(
+            kind="line", title="Rolling Per Era Correlation Mean", figsize=(15, 4)
+        )
+        plt.axhline(y=0.0, color="black", linestyle="-", linewidth=1)
+        plt.axhline(y=era_scores.mean(), color="g", linewidth=1, linestyle="--", label='Mean corr')
+        plt.show()
+
+        era_scores.cumsum().plot(title="Cumulative Sum of Era Scores", figsize=(15, 4))
+        plt.axhline(y=0.0, color="r", linestyle="--")
+        plt.show()
+
+    return pd.Series(metrics).round(4)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="numereval",
-    version="0.2",
+    version="0.2.5",
     author="Suraj Parmar",
     author_email="[email protected]",
     description="A small package for evaluating numer.ai model locally",