update market flow
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MuslemRahimi
parent
93f83a218b
commit
37917fd039
@ -9,15 +9,10 @@ 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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api_key = os.getenv('UNUSUAL_WHALES_API_KEY')
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fmp_api_key = os.getenv('FMP_API_KEY')
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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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ny_tz = pytz.timezone('America/New_York')
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@ -30,12 +25,7 @@ def save_json(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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# Replace 'Z' with '+00:00' to make it a valid ISO format
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iso_timestamp = entry['timestamp'].replace('Z', '+00:00')
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# Parse timestamp and convert to New York time
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timestamp = datetime.fromisoformat(iso_timestamp).astimezone(ny_tz)
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formatted_time = timestamp.strftime('%Y-%m-%d %H:%M:%S')
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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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@ -191,35 +181,116 @@ async def get_stock_chart_data(ticker):
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else:
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return []
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def get_market_tide():
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ticker_list = ['SPY','XLB','XLC','XLE','XLF','XLI','XLK','XLP','XLRE','XLU','XLV','XLY']
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res_list = {}
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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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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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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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url = f"https://api.unusualwhales.com/api/market/{ticker}/etf-tide"
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response = requests.get(url, headers=headers)
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if response.status_code == 200:
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data = response.json().get('data', [])
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data = [{k: v for k, v in item.items() if k != "date"} for item in data]
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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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else:
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raise Exception(f"Error fetching market tide data: {response.status_code}")
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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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# Combine SPY data and market tide data
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data = add_close_to_data(price_list, data)
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data = convert_timestamps(data)
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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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res_list[ticker] = data
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return res_list
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def get_top_sector_tickers():
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@ -335,15 +406,16 @@ def get_top_spy_tickers():
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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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