149 lines
6.1 KiB
Python
149 lines
6.1 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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def prepare_data(data, symbol):
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res_list = []
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#data = [entry for entry in data if datetime.strptime(entry['date'], "%Y-%m-%d") >= N_days_ago]
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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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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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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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# 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'] = df_change_dict[item['date']]
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if item['date'] in df_close_dict:
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new_item['price'] = df_close_dict[item['date']]
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res_list.append(new_item)
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except:
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pass
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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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except:
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res_list[i]['changesPercentageOI'] = 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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raise FileNotFoundError(f"The directory '{directory}' does not exist.")
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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(f"An error occurred: {e}")
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return []
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def aggregate_data_by_date():
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total_symbols = ['AA']
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data_by_date = defaultdict(lambda: {"volume": 0, "open_interest": 0})
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contracts_processed = 0
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for symbol in tqdm(total_symbols, desc="Processing symbols"):
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try:
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contract_list = get_contracts_from_directory(f"json/all-options-contracts/{symbol}")
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for item in tqdm(contract_list, desc=f"Processing {symbol} contracts", leave=False):
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try:
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with open(f"json/all-options-contracts/{symbol}/{item}.json", "r") as file:
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data = orjson.loads(file.read())
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# Process historical data
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for entry in data.get('history', []):
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date = entry.get('date')
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volume = entry.get('volume')
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open_interest = entry.get('open_interest')
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if date:
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# Aggregate volume
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if volume is not None:
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data_by_date[date]["volume"] += int(volume)
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# Aggregate open interest
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if open_interest is not None:
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data_by_date[date]["open_interest"] += int(open_interest)
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contracts_processed += 1
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except Exception as e:
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print(f"Error processing contract {item} for {symbol}: {e}")
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continue
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except Exception as e:
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print(f"Error processing symbol {symbol}: {e}")
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continue
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# Sort results by date
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sorted_results = {date: metrics for date, metrics in sorted(data_by_date.items())}
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return sorted_results, contracts_processed
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if __name__ == '__main__':
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# Run the aggregation
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results, total_processed = aggregate_data_by_date()
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print("\nData by date:")
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for date, metrics in results.items():
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print(f"{date}: Volume = {metrics['volume']:,}, Open Interest = {metrics['open_interest']:,}")
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print(f"\nTotal contracts processed: {total_processed}") |