148 lines
5.0 KiB
Python
148 lines
5.0 KiB
Python
import sqlite3
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from datetime import datetime, timedelta, date
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import ujson
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import os
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import numpy as np
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from dotenv import load_dotenv
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from benzinga import financial_data
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from collections import defaultdict
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from tqdm import tqdm
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load_dotenv()
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api_key = os.getenv('BENZINGA_API_KEY')
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fin = financial_data.Benzinga(api_key)
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def save_json(symbol, data):
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with open(f"json/options-net-flow/companies/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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def calculate_moving_average(data, window_size):
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data = np.array(data, dtype=float)
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cumsum = np.cumsum(data)
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moving_avg = (cumsum[window_size - 1:] - np.concatenate(([0], cumsum[:-window_size]))) / window_size
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return moving_avg.tolist()
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def calculate_net_flow(data, window_size=20):
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date_data = defaultdict(lambda: {'price': [], 'netCall': 0, 'netPut': 0})
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for item in data:
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date = item['date']
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try:
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premium = float(item['cost_basis'])
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date_data[date]['price'].append(float(item['underlying_price']))
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#date_data[date]['volume'] += volume
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if item['put_call'] == 'CALL':
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if item['execution_estimate'] == 'AT_ASK':
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date_data[date]['netCall'] += premium
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elif item['execution_estimate'] == 'AT_BID':
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date_data[date]['netCall'] -= premium
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elif item['put_call'] == 'PUT':
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if item['execution_estimate'] == 'AT_ASK':
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date_data[date]['netPut'] -= premium
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elif item['execution_estimate'] == 'AT_BID':
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date_data[date]['netPut'] += premium
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except:
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pass
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#volume = int(item['volume'])
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# Calculate average underlying price and format the results
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result = []
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for date, values in date_data.items():
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avg_price = sum(values['price']) / len(values['price'])
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#volume = values['volume']
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# Change sign of volume if netPut > netCall
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#if values['netPut'] > values['netCall']:
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# volume = -volume
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result.append({
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'date': date,
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'price': round(avg_price, 2),
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'netCall': int(values['netCall']),
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'netPut': int(values['netPut']),
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#'volume': int(volume)
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})
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sorted_data = sorted(result, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
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# Calculate moving averages
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netCall_values = [item['netCall'] for item in sorted_data]
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netPut_values = [item['netPut'] for item in sorted_data]
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netCall_ma = calculate_moving_average(netCall_values, window_size)
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netPut_ma = calculate_moving_average(netPut_values, window_size)
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# Add moving averages to the result and remove None values
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filtered_data = []
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# Add moving averages to the result
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filtered_data = []
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for i, item in enumerate(sorted_data):
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if i >= window_size - 1:
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item['netCall'] = int(netCall_ma[i - window_size + 1])
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item['netPut'] = int(netPut_ma[i - window_size + 1])
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filtered_data.append(item)
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return filtered_data
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def get_data(symbol):
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try:
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end_date = date.today()
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start_date = end_date - timedelta(200)
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end_date_str = end_date.strftime('%Y-%m-%d')
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start_date_str = start_date.strftime('%Y-%m-%d')
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res_list = []
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for page in range(0, 100):
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try:
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data = fin.options_activity(company_tickers=symbol, page=page, pagesize=1000, date_from=start_date_str, date_to=end_date_str)
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data = ujson.loads(fin.output(data))['option_activity']
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res_list += data
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except:
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break
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res_filtered = [{key: value for key, value in item.items() if key in ['ticker','date','execution_estimate', 'underlying_price', 'put_call', 'cost_basis']} for item in res_list]
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#Save raw data for each ticker for options page stack bar chart
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ticker_filtered_data = [entry for entry in res_filtered if entry['ticker'] == symbol]
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if len(ticker_filtered_data) > 100:
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net_flow_data = calculate_net_flow(ticker_filtered_data)
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if len(net_flow_data) > 0:
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save_json(symbol, net_flow_data)
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except ValueError as ve:
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print(ve)
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except Exception as e:
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print(e)
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try:
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stock_con = sqlite3.connect('stocks.db')
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stock_cursor = stock_con.cursor()
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stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%'")
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stock_symbols = [row[0] for row in stock_cursor.fetchall()]
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etf_con = sqlite3.connect('etf.db')
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etf_cursor = etf_con.cursor()
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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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stock_con.close()
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etf_con.close()
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total_symbols = stock_symbols + etf_symbols
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for symbol in tqdm(total_symbols):
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get_data(symbol)
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except Exception as e:
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print(e)
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