update cron jobs
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MuslemRahimi
parent
c618253767
commit
6e94223d3b
@ -7,6 +7,8 @@ import sqlite3
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from datetime import datetime, timedelta
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import pytz
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from typing import List, Dict
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import sqlite3
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from tqdm import tqdm
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def save_json(data, symbol):
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@ -36,87 +38,113 @@ def get_last_7_weekdays():
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def analyze_dark_pool_levels(trades: List[Dict],
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size_threshold: float = 0.8,
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price_grouping: float = 1.0) -> Dict:
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# Convert to DataFrame for easier manipulation
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df = pd.DataFrame(trades)
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# Convert premium strings to float values
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df['premium'] = df['premium'].apply(lambda x: float(str(x).replace(',', '')))
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# Round prices to group nearby levels
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df['price_level'] = (df['price'] / price_grouping).round(2) * price_grouping
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# Group by price level and sum volumes
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size_by_price = df.groupby('price_level').agg({
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'size': 'sum',
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'premium': 'sum'
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}).reset_index()
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# Calculate volume threshold
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min_size = size_by_price['size'].quantile(size_threshold)
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# Identify significant levels
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significant_levels = size_by_price[size_by_price['size'] >= min_size]
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# Sort levels by volume to get strongest levels first
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significant_levels = significant_levels.sort_values('size', ascending=False)
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# Separate into support and resistance based on current price
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current_price = df['price'].iloc[-1]
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support_levels = significant_levels[
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significant_levels['price_level'] < current_price
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].to_dict('records')
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resistance_levels = significant_levels[
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significant_levels['price_level'] > current_price
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].to_dict('records')
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# Calculate additional metrics
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metrics = {
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'avgTradeSize': round(df['size'].mean(),2),
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'totalPrem': round(df['premium'].sum(),2),
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'avgPremTrade': round(df['premium'].mean(),2)
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}
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price_level = support_levels+resistance_levels
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price_level = sorted(price_level, key=lambda x: float(x['price_level']))
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return {
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'price_level': price_level,
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'metrics': metrics,
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}
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data = []
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weekdays = get_last_7_weekdays()
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for date in weekdays:
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size_threshold: float = 0.8,
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price_grouping: float = 1.0) -> Dict:
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if not trades or not isinstance(trades, list):
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return {}
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try:
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with open(f"json/dark-pool/historical-flow/{date}.json", "r") as file:
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raw_data = orjson.loads(file.read())
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data +=raw_data
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except:
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pass
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df = pd.DataFrame(trades)
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if df.empty:
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return {}
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symbol = "GME"
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res_list = [item for item in data if item['ticker'] == symbol]
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# Ensure necessary columns exist
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if 'premium' not in df or 'price' not in df or 'size' not in df:
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return {}
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# Convert premium strings to float values
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df['premium'] = df['premium'].apply(lambda x: float(str(x).replace(',', '')))
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df['price_level'] = (df['price'] / price_grouping).round(1) * price_grouping
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size_by_price = df.groupby('price_level').agg({
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'size': 'sum',
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'premium': 'sum'
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}).reset_index()
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min_size = size_by_price['size'].quantile(size_threshold)
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significant_levels = size_by_price[size_by_price['size'] >= min_size]
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significant_levels = significant_levels.sort_values('size', ascending=False)
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current_price = df['price'].iloc[-1]
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support_levels = significant_levels[significant_levels['price_level'] < current_price].to_dict('records')
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resistance_levels = significant_levels[significant_levels['price_level'] > current_price].to_dict('records')
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metrics = {
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'avgTradeSize': round(df['size'].mean(), 2),
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'totalPrem': round(df['premium'].sum(), 2),
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'avgPremTrade': round(df['premium'].mean(), 2)
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}
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price_level = support_levels + resistance_levels
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price_level = sorted(price_level, key=lambda x: float(x['price_level']))
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return {
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'price_level': price_level,
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'metrics': metrics,
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}
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except Exception as e:
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print(f"Error analyzing dark pool levels: {e}")
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return {}
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dark_pool_levels = analyze_dark_pool_levels(
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trades=res_list,
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size_threshold=0.9, # Look for levels with volume in top 20%
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price_grouping=1.0 # Group prices within $1.00
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)
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print(dark_pool_levels['metrics'])
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def run():
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 1E9 AND symbol NOT LIKE '%.%'")
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stocks_symbols = [row[0] for row in cursor.fetchall()]
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etf_cursor = etf_con.cursor()
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etf_cursor.execute("PRAGMA journal_mode = wal")
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etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
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etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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con.close()
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etf_con.close()
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total_symbols = stocks_symbols+ etf_symbols
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data = []
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weekdays = get_last_7_weekdays()
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for date in weekdays:
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try:
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with open(f"json/dark-pool/historical-flow/{date}.json", "r") as file:
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raw_data = orjson.loads(file.read())
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data +=raw_data
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except:
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pass
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for symbol in tqdm(total_symbols):
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try:
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res_list = [item for item in data if isinstance(item, dict) and item.get('ticker', None) == symbol]
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dark_pool_levels = analyze_dark_pool_levels(
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trades=res_list,
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size_threshold=0.8, # Look for levels with volume in top 20%
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price_grouping=1.0 # Group prices within $1.00
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)
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if dark_pool_levels.get('price_level'): # Ensure there are valid levels
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top_5_elements = [
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{k: v for k, v in item.items() if k not in ['ticker', 'sector', 'assetType']}
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for item in sorted(res_list, key=lambda x: float(x.get('premium', 0)), reverse=True)[:5]
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]
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# Add rank to each item
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for rank, item in enumerate(top_5_elements, 1):
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item['rank'] = rank
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data_to_save = {
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'hottestTrades': top_5_elements,
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'priceLevel': dark_pool_levels['price_level'],
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'metrics': dark_pool_levels['metrics']
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}
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save_json(data_to_save, symbol)
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except Exception as e:
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print(f"Error processing {symbol}: {e}")
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top_5_elements = [{k: v for k, v in item.items() if k not in ['ticker', 'sector', 'assetType']} for item in sorted(res_list, key=lambda x: float(x['premium']), reverse=True)[:5]]
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# Add rank to each item
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for rank, item in enumerate(top_5_elements, 1):
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item['rank'] = rank
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data = {'hottestTrades': top_5_elements, 'priceLevel': dark_pool_levels['price_level'], 'metrics': dark_pool_levels['metrics']}
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if len(data) > 0:
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save_json(data, symbol)
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#print(data)
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if __name__ == "__main__":
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run()
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@ -65,6 +65,19 @@ def run_dark_pool_flow():
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if week <= 4 and 8 <= hour < 20:
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run_command(["python3", "cron_dark_pool_flow.py"])
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def run_dark_pool_level():
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now = datetime.now(ny_tz)
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week = now.weekday()
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hour = now.hour
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if week <= 4 and 8 <= hour < 20:
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run_command(["python3", "cron_dark_pool_level.py"])
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def run_dark_pool_ticker():
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now = datetime.now(ny_tz)
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week = now.weekday()
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if week <= 5:
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run_command(["python3", "cron_dark_pool_ticker.py"])
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def run_fda_calendar():
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now = datetime.now(ny_tz)
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week = now.weekday()
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@ -329,10 +342,10 @@ schedule.every().day.at("05:00").do(run_threaded, run_options_gex).tag('options_
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schedule.every().day.at("05:00").do(run_threaded, run_export_price).tag('export_price_job')
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schedule.every().day.at("06:00").do(run_threaded, run_historical_price).tag('historical_job')
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schedule.every().day.at("06:30").do(run_threaded, run_ai_score).tag('ai_score_job')
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schedule.every().day.at("07:00").do(run_threaded, run_ta_rating).tag('ta_rating_job')
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schedule.every().day.at("08:00").do(run_threaded, run_dark_pool_ticker).tag('dark_pool_ticker_job')
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schedule.every().day.at("09:00").do(run_threaded, run_hedge_fund).tag('hedge_fund_job')
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schedule.every().day.at("07:30").do(run_threaded, run_financial_statements).tag('financial_statements_job')
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schedule.every().day.at("08:00").do(run_threaded, run_economy_indicator).tag('economy_indicator_job')
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@ -384,6 +397,7 @@ schedule.every(3).hours.do(run_threaded, run_press_releases).tag('press_release_
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schedule.every(1).hours.do(run_threaded, run_fda_calendar).tag('fda_calendar_job')
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schedule.every(5).minutes.do(run_threaded, run_dark_pool_level).tag('dark_pool_level_job')
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schedule.every(10).seconds.do(run_threaded, run_dark_pool_flow).tag('dark_pool_flow_job')
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schedule.every(2).minutes.do(run_threaded, run_dashboard).tag('dashboard_job')
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