From 00bbf311acd7ca925fa7782a70698880b28ac2eb Mon Sep 17 00:00:00 2001
From: cardosofede <federico.cardoso.e@gmail.com>
Date: Wed, 12 Jul 2023 15:31:45 +0200
Subject: [PATCH] (feat) add statistical arbitrage strategy

---
 .../strategy/mean_reversion/stat_arb.py       | 51 +++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 quants_lab/strategy/mean_reversion/stat_arb.py

diff --git a/quants_lab/strategy/mean_reversion/stat_arb.py b/quants_lab/strategy/mean_reversion/stat_arb.py
new file mode 100644
index 0000000..3c6f629
--- /dev/null
+++ b/quants_lab/strategy/mean_reversion/stat_arb.py
@@ -0,0 +1,51 @@
+import pandas as pd
+import pandas_ta as ta
+from quants_lab.strategy.directional_strategy_base import DirectionalStrategyBase
+
+from quants_lab.utils import data_management
+
+
+class StatArb(DirectionalStrategyBase):
+    def __init__(self,
+                 exchange="binance_perpetual",
+                 trading_pair="DOGE-USDT",
+                 target_trading_pair="BTC-USDT",
+                 interval="1h",
+                 periods=100,
+                 deviation_threshold=1.1):
+        self.exchange = exchange
+        self.trading_pair = trading_pair
+        self.interval = interval
+        self.target_trading_pair = target_trading_pair
+        self.periods = periods
+        self.deviation_threshold = deviation_threshold
+
+    def get_raw_data(self):
+        df = data_management.get_dataframe(
+            exchange=self.exchange,
+            trading_pair=self.trading_pair,
+            interval=self.interval,
+        )
+        df_target = data_management.get_dataframe(
+            exchange=self.exchange,
+            trading_pair=self.target_trading_pair,
+            interval=self.interval,
+        )
+        df = pd.merge(df, df_target, on="timestamp", how='inner', suffixes=('', '_target'))
+        return df
+
+    def add_indicators(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"]
+        df["cum_spread"] = df["spread"].rolling(self.periods).sum()
+        df["z_score"] = ta.zscore(df["cum_spread"], length=self.periods)
+        return df
+
+    def add_signals(self, df):
+        df["side"] = 0
+        short_condition = df["z_score"] < - self.deviation_threshold
+        long_condition = df["z_score"] > self.deviation_threshold
+        df.loc[long_condition, "side"] = 1
+        df.loc[short_condition, "side"] = -1
+        return df