423 lines
16 KiB
Python
423 lines
16 KiB
Python
import os
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import pandas as pd
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import orjson
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from dotenv import load_dotenv
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import sqlite3
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from datetime import datetime, timedelta
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from GetStartEndDate import GetStartEndDate
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import asyncio
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import aiohttp
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import pytz
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import requests # Add missing import
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from collections import defaultdict
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load_dotenv()
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fmp_api_key = os.getenv('FMP_API_KEY')
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ny_tz = pytz.timezone('America/New_York')
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def save_json(data):
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directory = "json/market-flow"
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os.makedirs(directory, exist_ok=True) # Ensure the directory exists
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with open(f"{directory}/data.json", 'wb') as file: # Use binary mode for orjson
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file.write(orjson.dumps(data))
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# Function to convert and match timestamps
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def add_close_to_data(price_list, data):
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for entry in data:
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formatted_time = entry['timestamp']
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# Match with price_list
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for price in price_list:
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if price['date'] == formatted_time:
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entry['close'] = price['close']
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break # Match found, no need to continue searching
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return data
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def convert_timestamps(data_list):
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ny_tz = pytz.timezone('America/New_York')
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for item in data_list:
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try:
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# Get the timestamp and split on '.'
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timestamp = item['timestamp']
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base_time = timestamp.split('.')[0]
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# Handle microseconds if present
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if '.' in timestamp:
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microseconds = timestamp.split('.')[1].replace('Z', '')
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microseconds = microseconds.ljust(6, '0') # Pad with zeros if needed
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base_time = f"{base_time}.{microseconds}"
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# Replace 'Z' with '+00:00' (for UTC)
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base_time = base_time.replace('Z', '+00:00')
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# Parse the timestamp
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dt = datetime.fromisoformat(base_time)
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# Ensure the datetime is timezone-aware (assumed to be UTC initially)
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if dt.tzinfo is None:
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dt = pytz.utc.localize(dt)
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# Convert the time to New York timezone (automatically handles DST)
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ny_time = dt.astimezone(ny_tz)
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# Optionally, format to include date and time
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item['timestamp'] = ny_time.strftime('%Y-%m-%d %H:%M:%S')
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except ValueError as e:
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raise ValueError(f"Invalid timestamp format: {item['timestamp']} - Error: {str(e)}")
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except Exception as e:
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raise Exception(f"Error processing timestamp: {item['timestamp']} - Error: {str(e)}")
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return data_list
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def safe_round(value):
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"""Attempt to convert a value to float and round it. Return the original value if not possible."""
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try:
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return round(float(value), 2)
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except (ValueError, TypeError):
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return value
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def calculate_neutral_premium(data_item):
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"""Calculate the neutral premium for a data item."""
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call_premium = float(data_item['call_premium'])
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put_premium = float(data_item['put_premium'])
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bearish_premium = float(data_item['bearish_premium'])
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bullish_premium = float(data_item['bullish_premium'])
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total_premiums = bearish_premium + bullish_premium
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observed_premiums = call_premium + put_premium
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neutral_premium = observed_premiums - total_premiums
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return safe_round(neutral_premium)
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def generate_time_intervals(start_time, end_time):
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"""Generate 1-minute intervals from start_time to end_time."""
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intervals = []
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current_time = start_time
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while current_time <= end_time:
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intervals.append(current_time.strftime('%Y-%m-%d %H:%M:%S'))
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current_time += timedelta(minutes=1)
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return intervals
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def get_sector_data():
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try:
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url = "https://api.unusualwhales.com/api/market/sector-etfs"
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response = requests.get(url, headers=headers)
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data = response.json().get('data', [])
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res_list = []
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processed_data = []
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for item in data:
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symbol = item['ticker']
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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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# Step 1: Replace 'full_name' with 'name' if needed
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new_item = {
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'name' if key == 'full_name' else key: safe_round(value)
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for key, value in item.items()
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if key != 'in_out_flow'
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}
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# Step 2: Replace 'name' values
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if str(new_item.get('name')) == 'Consumer Staples':
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new_item['name'] = 'Consumer Defensive'
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elif str(new_item.get('name')) == 'Consumer Discretionary':
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new_item['name'] = 'Consumer Cyclical'
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elif str(new_item.get('name')) == 'Health Care':
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new_item['name'] = 'Healthcare'
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elif str(new_item.get('name')) == 'Financials':
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new_item['name'] = 'Financial Services'
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elif str(new_item.get('name')) == 'Materials':
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new_item['name'] = 'Basic Materials'
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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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with open(f"json/quote/{symbol}.json") as file:
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quote_data = orjson.loads(file.read())
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new_item['price'] = round(quote_data.get('price', 0), 2)
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new_item['changesPercentage'] = round(quote_data.get('changesPercentage', 0), 2)
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#get prem tick data:
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'''
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if symbol != 'SPY':
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prem_tick_history = get_etf_tide(symbol)
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#if symbol == 'XLB':
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# print(prem_tick_history[10])
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new_item['premTickHistory'] = prem_tick_history
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'''
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processed_data.append(new_item)
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return processed_data
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except Exception as e:
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print(e)
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return []
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async def get_stock_chart_data(ticker):
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start_date_1d, end_date_1d = GetStartEndDate().run()
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start_date = start_date_1d.strftime("%Y-%m-%d")
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end_date = end_date_1d.strftime("%Y-%m-%d")
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url = f"https://financialmodelingprep.com/api/v3/historical-chart/1min/{ticker}?from={start_date}&to={end_date}&apikey={fmp_api_key}"
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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if response.status == 200:
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data = await response.json()
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data = sorted(data, key=lambda x: x['date'])
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return data
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else:
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return []
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def get_market_tide(interval_5m=False):
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ticker_list = ['SPY']
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res_list = []
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for ticker in ticker_list:
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with open("json/options-flow/feed/data.json", "r") as file:
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data = orjson.loads(file.read())
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# Filter and sort data
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ticker_options = [item for item in data if item['ticker'] == ticker]
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ticker_options.sort(key=lambda x: x['time'])
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# Track changes per interval
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delta_data = defaultdict(lambda: {
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'cumulative_net_call_premium': 0,
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'cumulative_net_put_premium': 0,
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'call_ask_vol': 0,
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'call_bid_vol': 0,
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'put_ask_vol': 0,
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'put_bid_vol': 0
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})
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for item in ticker_options:
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try:
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# Parse and standardize timestamp
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dt = datetime.strptime(f"{item['date']} {item['time']}", "%Y-%m-%d %H:%M:%S")
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# Truncate to start of minute (for 1m summaries)
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dt = dt.replace(second=0, microsecond=0)
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# Adjust for 5-minute intervals if needed
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if interval_5m:
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dt -= timedelta(minutes=dt.minute % 5)
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rounded_ts = dt.strftime("%Y-%m-%d %H:%M:%S")
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# Extract metrics
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cost = float(item.get("cost_basis", 0))
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sentiment = item.get("sentiment", "").lower()
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put_call = item.get("put_call", "").lower()
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vol = int(item.get("volume", 1))
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# Update premium metrics
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if put_call == "calls":
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if sentiment == "bullish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] += cost
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delta_data[rounded_ts]['call_ask_vol'] += vol
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elif sentiment == "bearish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] -= cost
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delta_data[rounded_ts]['call_bid_vol'] += vol
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elif put_call == "puts":
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if sentiment == "bullish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] -= cost
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delta_data[rounded_ts]['put_ask_vol'] += vol
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elif sentiment == "bearish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] += cost
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delta_data[rounded_ts]['put_bid_vol'] += vol
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except Exception as e:
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print(f"Error processing item: {e}")
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# Calculate cumulative values over time
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sorted_ts = sorted(delta_data.keys())
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cumulative = {
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'net_call_premium': 0,
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'net_put_premium': 0,
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'call_ask': 0,
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'call_bid': 0,
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'put_ask': 0,
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'put_bid': 0
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}
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for ts in sorted_ts:
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# Update cumulative values
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cumulative['net_call_premium'] += delta_data[ts]['cumulative_net_call_premium']
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cumulative['net_put_premium'] += delta_data[ts]['cumulative_net_put_premium']
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cumulative['call_ask'] += delta_data[ts]['call_ask_vol']
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cumulative['call_bid'] += delta_data[ts]['call_bid_vol']
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cumulative['put_ask'] += delta_data[ts]['put_ask_vol']
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cumulative['put_bid'] += delta_data[ts]['put_bid_vol']
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# Calculate derived metrics
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call_volume = cumulative['call_ask'] + cumulative['call_bid']
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put_volume = cumulative['put_ask'] + cumulative['put_bid']
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net_volume = (cumulative['call_ask'] - cumulative['call_bid']) - \
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(cumulative['put_ask'] - cumulative['put_bid'])
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res_list.append({
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'timestamp': ts,
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'ticker': ticker,
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'net_call_premium': cumulative['net_call_premium'],
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'net_put_premium': cumulative['net_put_premium'],
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'call_volume': call_volume,
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'put_volume': put_volume,
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'net_volume': net_volume
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})
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res_list.sort(key=lambda x: x['timestamp'])
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price_list = asyncio.run(get_stock_chart_data(ticker))
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if len(price_list) == 0:
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with open(f"json/one-day-price/{ticker}.json") as file:
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price_list = orjson.loads(file.read())
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data = add_close_to_data(price_list, res_list)
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return res_list
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def get_top_sector_tickers():
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keep_elements = ['price', 'ticker', 'name', 'changesPercentage','netPremium','netCallPremium','netPutPremium','gexRatio','gexNetChange','ivRank']
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sector_list = [
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"Basic Materials",
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"Communication Services",
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"Consumer Cyclical",
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"Consumer Defensive",
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"Energy",
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"Financial Services",
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"Healthcare",
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"Industrials",
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"Real Estate",
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"Technology",
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"Utilities",
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]
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headers = {
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"Accept": "application/json, text/plain",
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"Authorization": api_key
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}
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url = "https://api.unusualwhales.com/api/screener/stocks"
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res_list = {}
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for sector in sector_list:
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querystring = {
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'order': 'net_premium',
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'order_direction': 'desc',
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'sectors[]': sector
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}
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response = requests.get(url, headers=headers, params=querystring)
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data = response.json().get('data', [])
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updated_data = []
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for item in data[:10]:
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try:
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new_item = {key: safe_round(value) for key, value in item.items()}
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with open(f"json/quote/{item['ticker']}.json") as file:
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quote_data = orjson.loads(file.read())
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new_item['name'] = quote_data['name']
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new_item['price'] = round(float(quote_data['price']), 2)
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new_item['changesPercentage'] = round(float(quote_data['changesPercentage']), 2)
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new_item['ivRank'] = round(float(new_item['iv_rank']),2)
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new_item['gexRatio'] = new_item['gex_ratio']
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new_item['gexNetChange'] = new_item['gex_net_change']
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new_item['netCallPremium'] = new_item['net_call_premium']
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new_item['netPutPremium'] = new_item['net_put_premium']
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new_item['netPremium'] = abs(new_item['netCallPremium'] - new_item['netPutPremium'])
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# Filter new_item to keep only specified elements
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filtered_item = {key: new_item[key] for key in keep_elements if key in new_item}
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updated_data.append(filtered_item)
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except Exception as e:
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print(f"Error processing ticker {item.get('ticker', 'unknown')}: {e}")
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# Add rank to each item
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for rank, item in enumerate(updated_data, 1):
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item['rank'] = rank
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res_list[sector] = updated_data
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return res_list
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def get_top_spy_tickers():
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keep_elements = ['price', 'ticker', 'name', 'changesPercentage','netPremium','netCallPremium','netPutPremium','gexRatio','gexNetChange','ivRank']
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headers = {
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"Accept": "application/json, text/plain",
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"Authorization": api_key
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}
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url = "https://api.unusualwhales.com/api/screener/stocks"
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querystring = {"is_s_p_500":"true"}
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response = requests.get(url, headers=headers, params=querystring)
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data = response.json().get('data', [])
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updated_data = []
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for item in data[:10]:
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try:
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new_item = {key: safe_round(value) for key, value in item.items()}
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with open(f"json/quote/{item['ticker']}.json") as file:
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quote_data = orjson.loads(file.read())
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new_item['name'] = quote_data['name']
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new_item['price'] = round(float(quote_data['price']), 2)
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new_item['changesPercentage'] = round(float(quote_data['changesPercentage']), 2)
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new_item['ivRank'] = round(float(new_item['iv_rank']),2)
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new_item['gexRatio'] = new_item['gex_ratio']
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new_item['gexNetChange'] = new_item['gex_net_change']
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new_item['netCallPremium'] = new_item['net_call_premium']
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new_item['netPutPremium'] = new_item['net_put_premium']
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new_item['netPremium'] = abs(new_item['netCallPremium'] - new_item['netPutPremium'])
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# Filter new_item to keep only specified elements
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filtered_item = {key: new_item[key] for key in keep_elements if key in new_item}
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updated_data.append(filtered_item)
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except Exception as e:
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print(f"Error processing ticker {item.get('ticker', 'unknown')}: {e}")
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# Add rank to each item
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for rank, item in enumerate(updated_data, 1):
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item['rank'] = rank
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return updated_data
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def main():
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market_tide = get_market_tide()
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data = {'marketTide': market_tide}
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'''
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sector_data = get_sector_data()
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top_sector_tickers = get_top_sector_tickers()
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top_spy_tickers = get_top_spy_tickers()
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top_sector_tickers['SPY'] = top_spy_tickers
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data = {'sectorData': sector_data, 'topSectorTickers': top_sector_tickers, 'marketTide': market_tide}
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'''
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if len(data) > 0:
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save_json(data)
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if __name__ == '__main__':
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main()
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