refactor: Standardize scanner implementations using utility functions
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@ -1,10 +1,8 @@
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from screener.user_input import get_interval_choice, get_date_range
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import os
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from datetime import datetime, timedelta
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import pandas as pd
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from db.db_connection import create_client
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from trading.position_calculator import PositionCalculator
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from utils.data_utils import get_stock_data, validate_signal_date, print_signal, save_signals_to_csv, get_qualified_stocks
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from utils.data_utils import (
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get_stock_data, validate_signal_date, print_signal,
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save_signals_to_csv, get_qualified_stocks,
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initialize_scanner, process_signal_data
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)
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from indicators.three_atr_ema import ThreeATREMAIndicator
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def check_entry_signal(df: pd.DataFrame) -> list:
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@ -61,74 +59,34 @@ def check_entry_signal(df: pd.DataFrame) -> list:
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return signals
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def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
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print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
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# Get time interval
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interval = get_interval_choice()
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start_date, end_date = get_date_range()
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start_ts = int(start_date.timestamp() * 1000000000)
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end_ts = int(end_date.timestamp() * 1000000000)
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try:
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qualified_stocks = get_qualified_stocks(start_date, end_date, min_price, max_price, min_volume)
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# Initialize scanner components
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interval, start_date, end_date, qualified_stocks, calculator = initialize_scanner(
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min_price, max_price, min_volume, portfolio_size
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)
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if not qualified_stocks:
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print("No stocks found matching criteria.")
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return
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print(f"\nFound {len(qualified_stocks)} stocks matching criteria")
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# Initialize indicators
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indicator = ThreeATREMAIndicator()
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calculator = None
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if portfolio_size and portfolio_size > 0:
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calculator = PositionCalculator(
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account_size=portfolio_size,
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risk_percentage=1.0,
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stop_loss_percentage=7.0 # Explicitly set 7% stop
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)
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bullish_signals = []
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for ticker, current_price, current_volume, last_update, stock_type in qualified_stocks:
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try:
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# Get historical data based on interval
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df = get_stock_data(ticker, start_date, end_date, interval)
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if df.empty or len(df) < 50: # Need at least 50 bars for the indicator
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if df.empty or len(df) < 21: # Need at least 21 bars for EMA
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continue
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results = indicator.calculate(df)
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# Check for signals throughout the date range
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signals = check_entry_signal(df)
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for signal, signal_date, signal_data in signals:
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entry_data = {
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'ticker': ticker,
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'entry_price': signal_data['price'],
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'target_price': signal_data['ema'],
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'volume': current_volume,
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'signal_date': signal_date,
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'stock_type': stock_type, # Add stock type
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'last_update': datetime.fromtimestamp(last_update/1000000000)
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}
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if calculator:
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position = calculator.calculate_position_size(entry_data['entry_price'])
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potential_profit = (entry_data['target_price'] - entry_data['entry_price']) * position['shares']
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entry_data.update({
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'shares': position['shares'],
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'position_size': position['position_value'],
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'stop_loss': position['stop_loss'],
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'risk_amount': position['potential_loss'],
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'profit_amount': potential_profit,
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'risk_reward_ratio': abs(potential_profit / position['potential_loss']) if position['potential_loss'] != 0 else 0
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})
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signal_data['date'] = signal_date
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entry_data = process_signal_data(
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ticker, signal_data, current_volume,
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last_update, stock_type, calculator
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)
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bullish_signals.append(entry_data)
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print_signal(entry_data, "🟢")
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except Exception as e:
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print(f"Error processing {ticker}: {str(e)}")
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continue
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@ -1,8 +1,8 @@
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import os
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import numpy as np
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from datetime import datetime, timedelta
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import pandas as pd
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from db.db_connection import create_client
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from utils.data_utils import (
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get_stock_data, validate_signal_date, print_signal,
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save_signals_to_csv, get_qualified_stocks,
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initialize_scanner, process_signal_data
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)
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from indicators.sunny_bands import SunnyBands
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from trading.position_calculator import PositionCalculator
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from screener.user_input import get_interval_choice, get_date_range
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@ -200,86 +200,39 @@ def view_stock_details(ticker: str, interval: str, start_date: datetime, end_dat
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print(f"Error analyzing {ticker}: {str(e)}")
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def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
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print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
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interval = get_interval_choice()
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# Get date range from user input
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start_date, end_date = get_date_range()
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# First get qualified stocks from database
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# Convert dates to Unix timestamp in nanoseconds
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end_ts = int(end_date.timestamp() * 1000000000)
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start_ts = int(start_date.timestamp() * 1000000000)
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try:
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qualified_stocks = get_qualified_stocks(start_date, end_date, min_price, max_price, min_volume)
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# Initialize scanner components
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interval, start_date, end_date, qualified_stocks, calculator = initialize_scanner(
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min_price, max_price, min_volume, portfolio_size
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)
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if not qualified_stocks:
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print("No stocks found matching criteria.")
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return
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print(f"\nFound {len(qualified_stocks)} stocks matching criteria")
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# Initialize indicators
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sunny = SunnyBands()
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calculator = None
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if portfolio_size and portfolio_size > 0:
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calculator = PositionCalculator(
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account_size=portfolio_size,
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risk_percentage=1.0,
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stop_loss_percentage=7.0 # Explicit 7% stop loss
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)
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bullish_signals = []
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bearish_signals = []
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# Process each qualified stock
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for ticker, current_price, current_volume, last_update, stock_type in qualified_stocks:
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try:
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# Get historical data based on interval
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df = get_stock_data(ticker, start_date, end_date, interval)
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if df.empty or len(df) < 50: # Need at least 50 bars for the indicator
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continue
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# Check for signals throughout the date range
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signals = check_entry_signal(df)
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for signal, signal_date, signal_data in signals:
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if calculator:
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try:
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position = calculator.calculate_position_size(
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entry_price=signal_data['price'],
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target_price=signal_data['upper_band']
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)
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if position['shares'] > 0:
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entry_data = {
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'ticker': ticker,
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'entry_price': signal_data['price'],
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'target_price': signal_data['upper_band'],
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'signal_date': signal_date,
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'volume': current_volume,
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'stock_type': stock_type,
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'last_update': datetime.fromtimestamp(last_update/1000000000),
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'shares': position['shares'],
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'position_size': position['position_value'],
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'stop_loss': signal_data['price'] * 0.93, # 7% stop loss
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'risk_amount': position['potential_loss'],
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'profit_amount': position['potential_profit'],
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'risk_reward_ratio': position['risk_reward_ratio']
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}
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bullish_signals.append(entry_data)
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print_signal(entry_data, "🟢")
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except ValueError as e:
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print(f"Skipping {ticker} position: {str(e)}")
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continue
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signal_data['date'] = signal_date
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entry_data = process_signal_data(
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ticker, signal_data, current_volume,
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last_update, stock_type, calculator
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)
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bullish_signals.append(entry_data)
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print_signal(entry_data, "🟢")
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except Exception as e:
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print(f"Error processing {ticker}: {str(e)}")
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continue
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save_signals_to_csv(bullish_signals, 'sunny')
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except Exception as e:
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print(f"Error during scan: {str(e)}")
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