feat: rm all preds

auto_trading/predictor/mean_agg.py

@@ -1,16 +0,0 @@
-import pandas as pd  # type: ignore
-
-from ..interfaces import Predictor
-
-
-class MeanAggregator(Predictor):
-    """Aggregate multiples predictors."""
-    
-    def __init__(self, predictors):
-        """Initialize MeanAggregator."""
-        self.predictors = predictors
-    
-    def predict(self, data: pd.DataFrame) -> dict:
-        """Predict from others predictors."""
-        preds = [cls.predict(data) for cls in self.predictors]
-        return pd.DataFrame(preds).mean().to_dict()

auto_trading/predictor/normalized.py

@@ -1,11 +0,0 @@
-import pandas as pd  # type: ignore
-
-from ..interfaces import Predictor
-
-
-class NormalizedPredictor(Predictor):
-    def predict(self, data: pd.DataFrame) -> dict:
-        """It's just random"""
-        df = data.iloc[-1]
-        df = df/df.sum()
-        return df.to_dict()

auto_trading/predictor/random_predictor.py

@@ -1,13 +0,0 @@
-from random import random
-import pandas as pd  # type: ignore
-
-from ..interfaces import Predictor
-
-
-class RandomPredictor(Predictor):
-    def predict(self, data: pd.DataFrame) -> dict:
-        """It's just random"""
-        return {
-            k: random()*2-1
-            for k in data.columns
-        }

auto_trading/predictor/selector.py

@@ -1,13 +0,0 @@
-import pandas as pd  # type: ignore
-
-from ..interfaces import Predictor
-
-
-class SelectorPredictor(Predictor):
-    
-    def __init__(self, to_return) -> None:
-        self.to_return = to_return
-    
-    def predict(self, data: pd.DataFrame) -> dict:
-        """It's just random"""
-        return {**{k:0 for k in data.to_dict().keys()}, **self.to_return}