Create signals/strategy.py

signals/strategy.py

@@ -0,0 +1,46 @@
+# signals/strategy.py
+
+import pandas as pd
+
+def generate_buy_signals(data_4h, data_1h):
+    """
+    Generates buy signals based on specified criteria.
+
+    Parameters:
+    - data_4h: DataFrame containing 4-hour interval stock data with SMA and price columns.
+    - data_1h: DataFrame containing 1-hour interval stock data with SMA and price columns.
+
+    Returns:
+    - buy_signals: DataFrame containing timestamps and signals where buy conditions are met.
+    """
+    # Criteria 1 & 2 for 4-hour data
+    criteria_4h = (data_4h['SMA_21'] > data_4h['SMA_50'])
+
+    # Criteria 3 & 4 for 1-hour data
+    crossed_above = (data_1h['SMA_21'].shift(2) < data_1h['SMA_50'].shift(2)) & (data_1h['SMA_21'] > data_1h['SMA_50'])
+    was_below = (data_1h['SMA_21'].shift(15) < data_1h['SMA_50'].shift(15))
+
+    # Combine criteria
+    buy_signals = data_1h[crossed_above & was_below & criteria_4h.reindex(data_1h.index, method='nearest')]
+
+    return buy_signals[['SMA_21', 'SMA_50']]
+
+def generate_sell_signals(data_4h):
+    """
+    Generates sell signals based on specified criteria.
+
+    Parameters:
+    - data_4h: DataFrame containing 4-hour interval stock data with Bollinger Bands and price columns.
+
+    Returns:
+    - sell_signals: DataFrame containing timestamps and signals where sell conditions are met.
+    """
+    # Criteria for sell signal
+    crossed_above_bb = data_4h['Close'] > data_4h['BB_Upper']
+
+    sell_signals = data_4h[crossed_above_bb]
+
+    return sell_signals[['Close', 'BB_Upper']]
+
+# Example usage would require actual loaded data with the appropriate columns calculated.
+# This example assumes `data_4h` and `data_1h` DataFrames are prepared and include 'Close', 'SMA_21', 'SMA_50', and Bollinger Bands columns.