From c3e2b52fc9a61496fdb6abd38c83756ca569c4bd Mon Sep 17 00:00:00 2001
From: cardosofede <federico.cardoso.e@gmail.com>
Date: Thu, 20 Jul 2023 14:53:41 +0200
Subject: [PATCH] (feat) rename methods

---
 quants_lab/strategy/directional_strategy_base.py | 9 +++++----
 quants_lab/strategy/experiments/bollinger.py     | 4 ++--
 quants_lab/strategy/experiments/macd_bb.py       | 4 ++--
 quants_lab/strategy/experiments/stat_arb.py      | 4 ++--
 4 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/quants_lab/strategy/directional_strategy_base.py b/quants_lab/strategy/directional_strategy_base.py
index ca6d86a..b75d049 100644
--- a/quants_lab/strategy/directional_strategy_base.py
+++ b/quants_lab/strategy/directional_strategy_base.py
@@ -15,10 +15,10 @@ class DirectionalStrategyBase:
     def get_raw_data(self):
         raise NotImplemented
 
-    def add_indicators(self, df):
+    def preprocessing(self, df):
         raise NotImplemented
 
-    def add_signals(self, df):
+    def predict(self, df):
         raise NotImplemented
 
     @staticmethod
@@ -57,9 +57,10 @@ class DirectionalStrategyBase:
                         std_span, order_amount=100, leverage=20, initial_portfolio=1000,
                         taker_fee=0.0003, maker_fee=0.00012,
                         start: Optional[str] = None, end: Optional[str] = None):
+        # TODO: Evaluate to move the get data outside the backtesting to optimize the performance.
         df = self.get_data(start=start, end=end)
-        df = self.add_indicators(df)
-        df = self.add_signals(df)
+        df = self.preprocessing(df)
+        df = self.predict(df)
         df = triple_barrier_method(
             df=df,
             std_span=std_span,
diff --git a/quants_lab/strategy/experiments/bollinger.py b/quants_lab/strategy/experiments/bollinger.py
index 6a7a7e4..af252b8 100644
--- a/quants_lab/strategy/experiments/bollinger.py
+++ b/quants_lab/strategy/experiments/bollinger.py
@@ -29,11 +29,11 @@ class Bollinger(DirectionalStrategyBase):
         )
         return df
 
-    def add_indicators(self, df):
+    def preprocessing(self, df):
         df.ta.bbands(length=self.bb_length, std=self.bb_std, append=True)
         return df
 
-    def add_signals(self, df):
+    def predict(self, df):
         df["side"] = 0
         long_condition = df[f"BBP_{self.bb_length}_{self.bb_std}"] < self.bb_long_threshold
         short_condition = df[f"BBP_{self.bb_length}_{self.bb_std}"] > self.bb_short_threshold
diff --git a/quants_lab/strategy/experiments/macd_bb.py b/quants_lab/strategy/experiments/macd_bb.py
index fb691b9..f6712d6 100644
--- a/quants_lab/strategy/experiments/macd_bb.py
+++ b/quants_lab/strategy/experiments/macd_bb.py
@@ -35,12 +35,12 @@ class MACDBB(DirectionalStrategyBase):
         )
         return df
 
-    def add_indicators(self, df):
+    def preprocessing(self, df):
         df.ta.bbands(length=self.bb_length, std=self.bb_std, append=True)
         df.ta.macd(fast=self.fast_macd, slow=self.slow_macd, signal=self.signal_macd, append=True)
         return df
 
-    def add_signals(self, df):
+    def predict(self, df):
         bbp = df[f"BBP_{self.bb_length}_{self.bb_std}"]
         macdh = df[f"MACDh_{self.fast_macd}_{self.slow_macd}_{self.signal_macd}"]
         macd = df[f"MACD_{self.fast_macd}_{self.slow_macd}_{self.signal_macd}"]
diff --git a/quants_lab/strategy/experiments/stat_arb.py b/quants_lab/strategy/experiments/stat_arb.py
index 3c6f629..8ec47fd 100644
--- a/quants_lab/strategy/experiments/stat_arb.py
+++ b/quants_lab/strategy/experiments/stat_arb.py
@@ -34,7 +34,7 @@ class StatArb(DirectionalStrategyBase):
         df = pd.merge(df, df_target, on="timestamp", how='inner', suffixes=('', '_target'))
         return df
 
-    def add_indicators(self, df):
+    def preprocessing(self, df):
         df["pct_change_original"] = df["close"].pct_change()
         df["pct_change_target"] = df["close_target"].pct_change()
         df["spread"] = df["pct_change_target"] - df["pct_change_original"]
@@ -42,7 +42,7 @@ class StatArb(DirectionalStrategyBase):
         df["z_score"] = ta.zscore(df["cum_spread"], length=self.periods)
         return df
 
-    def add_signals(self, df):
+    def predict(self, df):
         df["side"] = 0
         short_condition = df["z_score"] < - self.deviation_threshold
         long_condition = df["z_score"] > self.deviation_threshold