From d0583ddcc36bcaa3c709aa0b80b28964212fb621 Mon Sep 17 00:00:00 2001 From: "Bobby Abellana (aider)" Date: Fri, 7 Feb 2025 00:28:58 -0800 Subject: [PATCH] refactor: Update sunny scanner to use intraday stock_prices table --- src/screener/t_sunnyband.py | 64 ++++++++++++++++++++++++++++--------- 1 file changed, 49 insertions(+), 15 deletions(-) diff --git a/src/screener/t_sunnyband.py b/src/screener/t_sunnyband.py index f1ccb91..41b0be2 100644 --- a/src/screener/t_sunnyband.py +++ b/src/screener/t_sunnyband.py @@ -1,4 +1,5 @@ import os +import os import numpy as np from datetime import datetime, timedelta import pandas as pd @@ -276,24 +277,41 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf interval = get_interval_choice() end_date = datetime.now() - start_date = end_date - timedelta(days=1) + start_date = end_date - timedelta(days=1) # Get last trading day - # First get the data from database + # First get qualified stocks from database client = create_client() - # Query to get stocks meeting criteria + # Convert dates to Unix timestamp in nanoseconds + end_ts = int(end_date.timestamp() * 1000000000) + start_ts = int(start_date.timestamp() * 1000000000) + + # Query to get stocks meeting criteria with their latest data query = f""" - SELECT DISTINCT ticker, close - FROM stock_db.stock_prices_daily - WHERE date = (SELECT max(date) FROM stock_db.stock_prices_daily) - AND close BETWEEN {min_price} AND {max_price} - AND volume >= {min_volume} + WITH latest_data AS ( + SELECT + ticker, + argMax(close, window_start) as last_close, + sum(volume) as total_volume, + max(window_start) as last_update + FROM stock_db.stock_prices + WHERE window_start BETWEEN {start_ts} AND {end_ts} + GROUP BY ticker + HAVING last_close BETWEEN {min_price} AND {max_price} + AND total_volume >= {min_volume} + ) + SELECT + ticker, + last_close, + total_volume, + last_update + FROM latest_data ORDER BY ticker """ try: result = client.query(query) - qualified_stocks = [(row[0], row[1]) for row in result.result_rows] + qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows] if not qualified_stocks: print("No stocks found matching criteria.") @@ -311,14 +329,18 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf bearish_signals = [] # Process each qualified stock - for ticker, current_price in qualified_stocks: + for ticker, current_price, current_volume, last_update in qualified_stocks: try: + # Get historical data based on interval df = get_stock_data(ticker, start_date, end_date, interval) - if df.empty or len(df) < 50: + + if df.empty or len(df) < 50: # Need at least 50 bars for the indicator continue + # Calculate SunnyBands results = sunny.calculate(df) + # Check for signals if results['bullish_signal'].iloc[-1]: target_price = results['upper_band'].iloc[-1] @@ -329,6 +351,8 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf 'ticker': ticker, 'entry': current_price, 'target': target_price, + 'volume': current_volume, + 'last_update': datetime.fromtimestamp(last_update/1000000000), 'shares': position['shares'], 'position_size': position['position_value'], 'stop_loss': position['stop_loss'], @@ -344,22 +368,32 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf elif results['bearish_signal'].iloc[-1]: bearish_signals.append({ 'ticker': ticker, - 'price': current_price + 'price': current_price, + 'volume': current_volume, + 'last_update': datetime.fromtimestamp(last_update/1000000000) }) print(f"\n🔴 {ticker} at ${current_price:.2f}") except Exception as e: + print(f"Error processing {ticker}: {str(e)}") continue # Save results - output_date = datetime.now().strftime("%Y%m%d") + output_dir = 'reports' + os.makedirs(output_dir, exist_ok=True) + output_date = datetime.now().strftime("%Y%m%d_%H%M") + if bullish_signals: df_bullish = pd.DataFrame(bullish_signals) - df_bullish.to_csv(f'reports/sunny_bullish_{output_date}.csv', index=False) + output_file = f'{output_dir}/sunny_bullish_{output_date}.csv' + df_bullish.to_csv(output_file, index=False) + print(f"\nSaved bullish signals to {output_file}") if bearish_signals: df_bearish = pd.DataFrame(bearish_signals) - df_bearish.to_csv(f'reports/sunny_bearish_{output_date}.csv', index=False) + output_file = f'{output_dir}/sunny_bearish_{output_date}.csv' + df_bearish.to_csv(output_file, index=False) + print(f"\nSaved bearish signals to {output_file}") print(f"\nFound {len(bullish_signals)} bullish and {len(bearish_signals)} bearish signals")