309 lines
12 KiB
Python
309 lines
12 KiB
Python
import os
|
|
import numpy as np
|
|
from datetime import datetime, timedelta
|
|
import pandas as pd
|
|
from db.db_connection import create_client
|
|
from indicators.sunny_bands import SunnyBands
|
|
from trading.position_calculator import PositionCalculator
|
|
from screener.user_input import get_interval_choice
|
|
from utils.data_utils import get_stock_data
|
|
|
|
|
|
|
|
def get_valid_tickers(min_price: float, max_price: float, min_volume: int, interval: str) -> list:
|
|
"""Get tickers that meet the price and volume criteria"""
|
|
client = create_client()
|
|
|
|
# Get the most recent trading day
|
|
today = datetime.now().date()
|
|
yesterday = today - timedelta(days=1)
|
|
|
|
# First get valid tickers from daily data
|
|
daily_query = f"""
|
|
SELECT DISTINCT ticker
|
|
FROM stock_db.stock_prices_daily
|
|
WHERE date = '{yesterday}'
|
|
AND close BETWEEN {min_price} AND {max_price}
|
|
AND volume >= {min_volume}
|
|
ORDER BY ticker ASC
|
|
"""
|
|
|
|
try:
|
|
result = client.query(daily_query)
|
|
tickers = [row[0] for row in result.result_rows]
|
|
|
|
print(f"\nFound {len(tickers)} stocks matching price and volume criteria")
|
|
|
|
if interval != "daily":
|
|
# Now verify these tickers have intraday data
|
|
# Convert to Unix timestamp in nanoseconds
|
|
start_ts = int(datetime.combine(yesterday, datetime.strptime("09:30", "%H:%M").time()).timestamp() * 1000000000)
|
|
end_ts = int(datetime.combine(yesterday, datetime.strptime("16:00", "%H:%M").time()).timestamp() * 1000000000)
|
|
|
|
intraday_query = f"""
|
|
SELECT DISTINCT ticker
|
|
FROM stock_db.stock_prices
|
|
WHERE ticker IN ({','.join([f"'{t}'" for t in tickers])})
|
|
AND window_start BETWEEN {start_ts} AND {end_ts}
|
|
GROUP BY ticker
|
|
HAVING count() >= 50 -- Ensure we have enough data points for the indicator
|
|
"""
|
|
|
|
result = client.query(intraday_query)
|
|
tickers = [row[0] for row in result.result_rows]
|
|
print(f"Of those, {len(tickers)} have recent intraday data")
|
|
|
|
return tickers
|
|
|
|
except Exception as e:
|
|
print(f"Error fetching tickers: {str(e)}")
|
|
return []
|
|
|
|
def view_stock_details(ticker: str, interval: str, start_date: datetime, end_date: datetime) -> None:
|
|
"""Display detailed data for a single stock"""
|
|
print(f"\n📊 Detailed Analysis for {ticker}")
|
|
print(f"Interval: {interval}")
|
|
print(f"Date Range: {start_date.date()} to {end_date.date()}")
|
|
|
|
try:
|
|
# Construct query
|
|
client = create_client()
|
|
today = datetime.now().date()
|
|
start_date = today - timedelta(days=60)
|
|
|
|
if interval == "daily":
|
|
table = "stock_prices_daily"
|
|
date_col = "date"
|
|
query = f"""
|
|
SELECT
|
|
{date_col} as date,
|
|
open,
|
|
high,
|
|
low,
|
|
close,
|
|
volume
|
|
FROM stock_db.{table}
|
|
WHERE ticker = '{ticker}'
|
|
AND {date_col} BETWEEN '{start_date}' AND '{today}'
|
|
ORDER BY date ASC
|
|
"""
|
|
else:
|
|
table = "stock_prices"
|
|
date_col = "window_start"
|
|
minutes_map = {
|
|
"5min": 5,
|
|
"15min": 15,
|
|
"30min": 30,
|
|
"1hour": 60
|
|
}
|
|
minutes = minutes_map[interval]
|
|
|
|
query = f"""
|
|
SELECT
|
|
fromUnixTimestamp(intDiv({date_col}, 300) * 300) as interval_start,
|
|
min(open) as open,
|
|
max(high) as high,
|
|
min(low) as low,
|
|
argMax(close, {date_col}) as close,
|
|
sum(volume) as volume
|
|
FROM stock_db.{table}
|
|
WHERE ticker = '{ticker}'
|
|
AND {date_col} BETWEEN toUnixTimestamp('{start_date}') AND toUnixTimestamp('{today}')
|
|
GROUP BY interval_start
|
|
ORDER BY interval_start ASC
|
|
"""
|
|
|
|
# Execute query and get results
|
|
result = client.query(query)
|
|
|
|
if not result.result_rows:
|
|
print("\nNo data found for this stock")
|
|
return
|
|
|
|
# Convert to DataFrame
|
|
df = pd.DataFrame(
|
|
result.result_rows,
|
|
columns=['date', 'open', 'high', 'low', 'close', 'volume']
|
|
)
|
|
|
|
# Print raw query results
|
|
print("\nRaw Query Results:")
|
|
print(f"Number of rows returned: {len(result.result_rows)}")
|
|
print("\nFirst 5 rows of raw data:")
|
|
for i, row in enumerate(result.result_rows[:5]):
|
|
print(f"Row {i + 1}: {row}")
|
|
|
|
print("\nLast 5 rows of raw data:")
|
|
for i, row in enumerate(result.result_rows[-5:]):
|
|
print(f"Row {len(result.result_rows) - 4 + i}: {row}")
|
|
|
|
# Calculate SunnyBands
|
|
sunny = SunnyBands()
|
|
results = sunny.calculate(df)
|
|
|
|
# Get last values
|
|
last_price = df.iloc[-1]
|
|
last_bands = results.iloc[-1]
|
|
|
|
print("\nLatest Values:")
|
|
print(f"Date: {last_price['date']}")
|
|
print(f"Open: ${last_price['open']:.2f}")
|
|
print(f"High: ${last_price['high']:.2f}")
|
|
print(f"Low: ${last_price['low']:.2f}")
|
|
print(f"Close: ${last_price['close']:.2f}")
|
|
print(f"Volume: {last_price['volume']:,}")
|
|
print(f"\nSunnyBands Indicators:")
|
|
print(f"DMA: ${last_bands['dma']:.2f}")
|
|
print(f"Upper Band: ${last_bands['upper_band']:.2f}")
|
|
print(f"Lower Band: ${last_bands['lower_band']:.2f}")
|
|
print(f"Bullish Signal: {'Yes' if last_bands['bullish_signal'] else 'No'}")
|
|
print(f"Bearish Signal: {'Yes' if last_bands['bearish_signal'] else 'No'}")
|
|
|
|
except Exception as e:
|
|
print(f"Error analyzing {ticker}: {str(e)}")
|
|
|
|
def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portfolio_size: float = None) -> None:
|
|
print(f"\nScanning for stocks ${min_price:.2f}-${max_price:.2f} with min volume {min_volume:,}")
|
|
|
|
interval = get_interval_choice()
|
|
end_date = datetime.now()
|
|
start_date = end_date - timedelta(days=1) # Get last trading day
|
|
|
|
# First get qualified stocks from database
|
|
client = create_client()
|
|
|
|
# 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"""
|
|
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], row[2], row[3]) 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.")
|
|
return
|
|
|
|
print(f"\nFound {len(qualified_stocks)} stocks matching criteria")
|
|
|
|
# Initialize indicators
|
|
sunny = SunnyBands()
|
|
calculator = None
|
|
if portfolio_size and portfolio_size > 0:
|
|
calculator = PositionCalculator(
|
|
account_size=portfolio_size,
|
|
risk_percentage=1.0,
|
|
stop_loss_percentage=7.0 # Explicit 7% stop loss
|
|
)
|
|
|
|
bullish_signals = []
|
|
bearish_signals = []
|
|
|
|
# Process each qualified stock
|
|
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: # 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]
|
|
|
|
if calculator:
|
|
try:
|
|
position = calculator.calculate_position_size(
|
|
entry_price=current_price,
|
|
target_price=target_price
|
|
)
|
|
if position['shares'] > 0:
|
|
# Update signal data with proper stop loss calculation
|
|
signal_data = {
|
|
'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': current_price * 0.93, # 7% stop loss
|
|
'risk': position['potential_loss'],
|
|
'reward': position['potential_profit'],
|
|
'r_r': position['risk_reward_ratio']
|
|
}
|
|
bullish_signals.append(signal_data)
|
|
# Update print output format
|
|
dollar_risk = position['potential_loss'] * -1
|
|
print(f"\n🟢 {ticker} @ ${current_price:.2f}")
|
|
print(f" Size: {position['shares']} shares (${position['position_value']:.2f})")
|
|
print(f" Stop: ${signal_data['stop_loss']:.2f} (-7%) | Target: ${target_price:.2f}")
|
|
print(f" Risk/Reward: 1:{position['risk_reward_ratio']:.1f} | Risk: ${dollar_risk:.2f}")
|
|
except ValueError as e:
|
|
print(f"Skipping {ticker} position: {str(e)}")
|
|
continue
|
|
|
|
elif results['bearish_signal'].iloc[-1]:
|
|
bearish_signals.append({
|
|
'ticker': ticker,
|
|
'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_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)
|
|
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)
|
|
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")
|
|
|
|
except Exception as e:
|
|
print(f"Error during scan: {str(e)}")
|