304 lines
11 KiB
Python
304 lines
11 KiB
Python
import requests
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import orjson
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import re
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from datetime import datetime,timedelta
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from dotenv import load_dotenv
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import os
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import sqlite3
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import pandas as pd
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import time
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from tqdm import tqdm
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from collections import defaultdict
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#today = datetime.today()
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#N_days_ago = today - timedelta(days=90)
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query_template = """
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SELECT date, close, change_percent
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FROM "{ticker}"
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WHERE date BETWEEN ? AND ?
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"""
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def save_json(data, symbol):
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directory_path = f"json/options-historical-data/companies"
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os.makedirs(directory_path, exist_ok=True) # Ensure the directory exists
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with open(f"{directory_path}/{symbol}.json", 'wb') as file: # Use binary mode for orjson
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file.write(orjson.dumps(data))
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def safe_round(value, decimals=2):
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try:
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return round(float(value), decimals)
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except (ValueError, TypeError):
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return value
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def aggregate_data_by_date(symbol):
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data_by_date = defaultdict(lambda: {
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"date": "",
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"call_volume": 0,
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"put_volume": 0,
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"call_open_interest": 0,
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"put_open_interest": 0,
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"call_premium": 0,
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"put_premium": 0,
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"call_gex": 0,
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"put_gex": 0,
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"call_dex": 0,
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"put_dex": 0,
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"iv": 0.0, # Sum of implied volatilities
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"iv_count": 0, # Count of entries for IV
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})
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# Calculate cutoff date (1 year ago)
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today = datetime.today().date()
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one_year_ago = today - timedelta(days=365)
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one_year_ago_str = one_year_ago.strftime('%Y-%m-%d')
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contract_dir = f"json/all-options-contracts/{symbol}"
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contract_list = get_contracts_from_directory(contract_dir)
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if len(contract_list) > 0:
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for item in contract_list:
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try:
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file_path = os.path.join(contract_dir, f"{item}.json")
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with open(file_path, "r") as file:
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data = orjson.loads(file.read())
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option_type = data.get('optionType', None)
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if option_type not in ['call', 'put']:
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continue
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for entry in data.get('history', []):
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date = entry.get('date')
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# Skip entries older than one year
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#if date < one_year_ago_str:
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# continue
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volume = entry.get('volume', 0) or 0
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open_interest = entry.get('open_interest', 0) or 0
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total_premium = entry.get('total_premium', 0) or 0
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implied_volatility = entry.get('implied_volatility', 0) or 0
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gamma = entry.get('gamma',0) or 0
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delta = entry.get('delta',0) or 0
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gex = 100 * open_interest *
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daily_data = data_by_date[date]
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daily_data["date"] = date
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if option_type == 'call':
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daily_data["call_volume"] += int(volume)
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daily_data["call_open_interest"] += int(open_interest)
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daily_data["call_premium"] += int(total_premium)
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elif option_type == 'put':
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daily_data["put_volume"] += int(volume)
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daily_data["put_open_interest"] += int(open_interest)
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daily_data["put_premium"] += int(total_premium)
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# Aggregate IV for both calls and puts
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daily_data["iv"] += round(implied_volatility, 2)
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daily_data["iv_count"] += 1
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# Calculate put/call ratio
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try:
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daily_data["putCallRatio"] = round(daily_data["put_volume"] / daily_data["call_volume"], 2)
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except ZeroDivisionError:
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daily_data["putCallRatio"] = None
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except Exception as e:
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print(f"Error processing {item}: {e}")
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continue
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# Convert to list and calculate average IV
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data = []
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for date, daily in data_by_date.items():
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if daily['iv_count'] > 0:
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daily['iv'] = round(daily['iv'] / daily['iv_count'], 2)
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else:
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daily['iv'] = None
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data.append(daily)
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# Sort and calculate IV Rank
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data = sorted(data, key=lambda x: x['date'])
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data = calculate_iv_rank_for_all(data)
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data = sorted(data, key=lambda x: x['date'], reverse=True)
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return data
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else:
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return []
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def calculate_iv_rank_for_all(data):
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if not data:
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return []
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# Convert to DataFrame
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df = pd.DataFrame(data)
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# Check if 'iv' exists and filter out entries without IV
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if 'iv' not in df.columns or df['iv'].isnull().all():
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for entry in data:
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entry['iv_rank'] = None
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return data
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# Convert date to datetime and sort
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df['date'] = pd.to_datetime(df['date'])
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df.sort_values('date', inplace=True)
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# Calculate rolling 365-day min and max for IV
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df.set_index('date', inplace=True)
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rolling_min = df['iv'].rolling('365D', min_periods=1).min()
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rolling_max = df['iv'].rolling('365D', min_periods=1).max()
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# Merge back into DataFrame
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df['rolling_min'] = rolling_min
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df['rolling_max'] = rolling_max
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# Calculate IV Rank
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df['iv_rank'] = ((df['iv'] - df['rolling_min']) / (df['rolling_max'] - df['rolling_min'])) * 100
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df['iv_rank'] = df['iv_rank'].round(2)
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# Handle cases where max == min
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df.loc[df['rolling_max'] == df['rolling_min'], 'iv_rank'] = 100.0
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# Replace NaN with None
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df['iv_rank'] = df['iv_rank'].where(pd.notnull(df['iv_rank']), None)
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# Drop temporary columns
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df.drop(['rolling_min', 'rolling_max'], axis=1, inplace=True)
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# Convert back to list of dicts
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df.reset_index(inplace=True)
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df['date'] = df['date'].dt.strftime('%Y-%m-%d')
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result = df.to_dict('records')
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# Sort in reverse chronological order
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result = sorted(result, key=lambda x: x['date'], reverse=True)
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return result
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def prepare_data(data, symbol):
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data = [entry for entry in data if entry['call_volume'] != 0 or entry['put_volume'] != 0]
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start_date_str = data[-1]['date']
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end_date_str = data[0]['date']
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query = query_template.format(ticker=symbol)
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df_price = pd.read_sql_query(query, con if symbol in stocks_symbols else etf_con, params=(start_date_str, end_date_str)).round(2)
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df_price = df_price.rename(columns={"change_percent": "changesPercentage"})
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# Convert the DataFrame to a dictionary for quick lookups by date
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df_change_dict = df_price.set_index('date')['changesPercentage'].to_dict()
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df_close_dict = df_price.set_index('date')['close'].to_dict()
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res_list = []
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for item in data:
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try:
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# Round numerical and numerical-string values
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new_item = {
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key: safe_round(value) if isinstance(value, (int, float, str)) else value
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for key, value in item.items()
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}
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# Add parsed fields
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new_item['volume'] = round(new_item['call_volume'] + new_item['put_volume'], 2)
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new_item['putCallRatio'] = round(new_item['put_volume']/new_item['call_volume'],2)
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#new_item['avgVolumeRatio'] = round(new_item['volume'] / (round(new_item['avg_30_day_call_volume'] + new_item['avg_30_day_put_volume'], 2)), 2)
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new_item['total_premium'] = round(new_item['call_premium'] + new_item['put_premium'], 2)
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#new_item['net_premium'] = round(new_item['net_call_premium'] - new_item['net_put_premium'],2)
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new_item['total_open_interest'] = round(new_item['call_open_interest'] + new_item['put_open_interest'], 2)
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#bearish_premium = float(item['bearish_premium'])
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#bullish_premium = float(item['bullish_premium'])
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#neutral_premium = calculate_neutral_premium(item)
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'''
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new_item['premium_ratio'] = [
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safe_round(bearish_premium),
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neutral_premium,
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safe_round(bullish_premium)
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]
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'''
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# Add changesPercentage if the date exists in df_change_dict
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if item['date'] in df_change_dict:
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new_item['changesPercentage'] = float(df_change_dict[item['date']])
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else:
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new_item['changesPercentage'] = None
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if item['date'] in df_close_dict:
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new_item['price'] = float(df_close_dict[item['date']])
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else:
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new_item['price'] = None
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res_list.append(new_item)
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except Exception as e:
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print(e)
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res_list = sorted(res_list, key=lambda x: x['date'])
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for i in range(1, len(res_list)):
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try:
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current_open_interest = res_list[i]['total_open_interest']
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previous_open_interest = res_list[i-1]['total_open_interest']
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changes_percentage_oi = round((current_open_interest/previous_open_interest -1)*100,2)
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res_list[i]['changesPercentageOI'] = changes_percentage_oi
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res_list[i]['changeOI'] = current_open_interest-previous_open_interest
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except:
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res_list[i]['changesPercentageOI'] = None
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res_list[i]['changeOI'] = None
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res_list = sorted(res_list, key=lambda x: x['date'],reverse=True)
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if res_list:
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save_json(res_list, symbol)
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def get_contracts_from_directory(directory: str):
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try:
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# Ensure the directory exists
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if not os.path.exists(directory):
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return []
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# Get all tickers from filenames
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return [file.replace(".json", "") for file in os.listdir(directory) if file.endswith(".json")]
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except Exception as e:
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print(e)
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return []
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# Connect to the databases
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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 symbol NOT LIKE '%.%' AND marketCap > 1E9")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE 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 WHERE marketCap > 1E9")
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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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total_symbols = stocks_symbols + etf_symbols
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for symbol in tqdm(total_symbols):
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try:
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data = aggregate_data_by_date(symbol)
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data = prepare_data(data, symbol)
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except:
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pass
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con.close()
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etf_con.close() |