286 lines
11 KiB
Python
286 lines
11 KiB
Python
import os
|
|
import pandas as pd
|
|
import orjson
|
|
from dotenv import load_dotenv
|
|
import sqlite3
|
|
from datetime import datetime, timedelta
|
|
import asyncio
|
|
import aiohttp
|
|
import pytz
|
|
import requests # Add missing import
|
|
from collections import defaultdict
|
|
from GetStartEndDate import GetStartEndDate
|
|
from tqdm import tqdm
|
|
|
|
import re
|
|
|
|
|
|
load_dotenv()
|
|
fmp_api_key = os.getenv('FMP_API_KEY')
|
|
|
|
|
|
ny_tz = pytz.timezone('America/New_York')
|
|
|
|
|
|
|
|
def save_json(data, filename):
|
|
directory = "json/market-flow"
|
|
os.makedirs(directory, exist_ok=True) # Ensure the directory exists
|
|
with open(f"{directory}/{filename}.json", 'wb') as file: # Use binary mode for orjson
|
|
file.write(orjson.dumps(data))
|
|
|
|
|
|
def safe_round(value):
|
|
try:
|
|
return round(float(value), 2)
|
|
except (ValueError, TypeError):
|
|
return value
|
|
|
|
# Function to convert and match timestamps
|
|
def add_close_to_data(price_list, data):
|
|
for entry in data:
|
|
formatted_time = entry['time']
|
|
# Match with price_list
|
|
for price in price_list:
|
|
if price['date'] == formatted_time:
|
|
entry['close'] = price['close']
|
|
break # Match found, no need to continue searching
|
|
return data
|
|
|
|
|
|
|
|
async def get_stock_chart_data(ticker):
|
|
start_date_1d, end_date_1d = GetStartEndDate().run()
|
|
start_date = start_date_1d.strftime("%Y-%m-%d")
|
|
end_date = end_date_1d.strftime("%Y-%m-%d")
|
|
|
|
url = f"https://financialmodelingprep.com/api/v3/historical-chart/1min/{ticker}?from={start_date}&to={end_date}&apikey={fmp_api_key}"
|
|
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.get(url) as response:
|
|
if response.status == 200:
|
|
data = await response.json()
|
|
data = sorted(data, key=lambda x: x['date'])
|
|
return data
|
|
else:
|
|
return []
|
|
|
|
|
|
|
|
|
|
def get_sector_data(sector_ticker,interval_1m=True):
|
|
res_list = []
|
|
|
|
# Load the options flow data.
|
|
with open("json/options-flow/feed/data.json", "r") as file:
|
|
all_data = orjson.loads(file.read())
|
|
|
|
# Load ETF holdings data and extract ticker weights.
|
|
with open(f"json/etf/holding/{sector_ticker}.json", "r") as file:
|
|
holdings_data = orjson.loads(file.read())
|
|
# Build a dictionary mapping ticker symbols to their weightPercentage.
|
|
ticker_weights = {item['symbol']: item['weightPercentage'] for item in holdings_data['holdings']}
|
|
|
|
# Use a common dictionary to accumulate flows across all tickers.
|
|
delta_data = defaultdict(lambda: {
|
|
'cumulative_net_call_premium': 0,
|
|
'cumulative_net_put_premium': 0,
|
|
'call_ask_vol': 0,
|
|
'call_bid_vol': 0,
|
|
'put_ask_vol': 0,
|
|
'put_bid_vol': 0
|
|
})
|
|
|
|
# Process each ticker's data using its weight.
|
|
for ticker in tqdm(ticker_weights.keys()):
|
|
# Convert the weight percentage to a fraction.
|
|
weight = 1 #ticker_weights[ticker] / 100.0 #ignore weights of sector
|
|
# Filter data for the current ticker.
|
|
ticker_data = [item for item in all_data if item.get('ticker') == ticker]
|
|
ticker_data.sort(key=lambda x: x['time'])
|
|
|
|
for item in ticker_data:
|
|
try:
|
|
# Combine date and time, then truncate seconds and microseconds.
|
|
dt = datetime.strptime(f"{item['date']} {item['time']}", "%Y-%m-%d %H:%M:%S")
|
|
dt = dt.replace(second=0, microsecond=0)
|
|
|
|
# Adjust to the start of the minute if using 1-minute intervals.
|
|
if interval_1m:
|
|
minute = dt.minute - (dt.minute % 1)
|
|
dt = dt.replace(minute=minute)
|
|
|
|
rounded_ts = dt.strftime("%Y-%m-%d %H:%M:%S")
|
|
|
|
# Extract metrics.
|
|
cost = float(item.get("cost_basis", 0))
|
|
sentiment = item.get("sentiment", "")
|
|
put_call = item.get("put_call", "")
|
|
vol = int(item.get("volume", 0))
|
|
|
|
# Update metrics, scaled by the ticker's weight.
|
|
if put_call == "Calls":
|
|
if sentiment == "Bullish":
|
|
delta_data[rounded_ts]['cumulative_net_call_premium'] += cost * weight
|
|
delta_data[rounded_ts]['call_ask_vol'] += vol * weight
|
|
elif sentiment == "Bearish":
|
|
delta_data[rounded_ts]['cumulative_net_call_premium'] -= cost * weight
|
|
delta_data[rounded_ts]['call_bid_vol'] += vol * weight
|
|
elif put_call == "Puts":
|
|
if sentiment == "Bullish":
|
|
delta_data[rounded_ts]['cumulative_net_put_premium'] += cost * weight
|
|
delta_data[rounded_ts]['put_ask_vol'] += vol * weight
|
|
elif sentiment == "Bearish":
|
|
delta_data[rounded_ts]['cumulative_net_put_premium'] -= cost * weight
|
|
delta_data[rounded_ts]['put_bid_vol'] += vol * weight
|
|
|
|
except Exception as e:
|
|
print(f"Error processing item: {e}")
|
|
|
|
# Calculate cumulative values over time.
|
|
sorted_ts = sorted(delta_data.keys())
|
|
cumulative = {
|
|
'net_call_premium': 0,
|
|
'net_put_premium': 0,
|
|
'call_ask': 0,
|
|
'call_bid': 0,
|
|
'put_ask': 0,
|
|
'put_bid': 0
|
|
}
|
|
|
|
for ts in sorted_ts:
|
|
cumulative['net_call_premium'] += delta_data[ts]['cumulative_net_call_premium']
|
|
cumulative['net_put_premium'] += delta_data[ts]['cumulative_net_put_premium']
|
|
cumulative['call_ask'] += delta_data[ts]['call_ask_vol']
|
|
cumulative['call_bid'] += delta_data[ts]['call_bid_vol']
|
|
cumulative['put_ask'] += delta_data[ts]['put_ask_vol']
|
|
cumulative['put_bid'] += delta_data[ts]['put_bid_vol']
|
|
|
|
call_volume = cumulative['call_ask'] + cumulative['call_bid']
|
|
put_volume = cumulative['put_ask'] + cumulative['put_bid']
|
|
net_volume = (cumulative['call_ask'] - cumulative['call_bid']) - (cumulative['put_ask'] - cumulative['put_bid'])
|
|
|
|
res_list.append({
|
|
'time': ts,
|
|
'net_call_premium': round(cumulative['net_call_premium']),
|
|
'net_put_premium': round(cumulative['net_put_premium']),
|
|
'call_volume': round(call_volume),
|
|
'put_volume': round(put_volume),
|
|
'net_volume': round(net_volume),
|
|
})
|
|
|
|
# Sort the results list by time.
|
|
res_list.sort(key=lambda x: x['time'])
|
|
|
|
# Get the price list for the sector ticker.
|
|
price_list = asyncio.run(get_stock_chart_data(sector_ticker))
|
|
if len(price_list) == 0:
|
|
with open(f"json/one-day-price/{sector_ticker}.json", "r") as file:
|
|
price_list = orjson.loads(file.read())
|
|
|
|
# Append closing prices to the data.
|
|
data = add_close_to_data(price_list, res_list)
|
|
|
|
# Ensure that each minute until the specified end time (e.g., 16:01:00) is present.
|
|
fields = ['net_call_premium', 'net_put_premium', 'call_volume', 'put_volume', 'net_volume', 'close']
|
|
last_time = datetime.strptime(data[-1]['time'], "%Y-%m-%d %H:%M:%S")
|
|
end_time = last_time.replace(hour=16, minute=1, second=0)
|
|
|
|
while last_time < end_time:
|
|
last_time += timedelta(minutes=1)
|
|
data.append({
|
|
'time': last_time.strftime("%Y-%m-%d %H:%M:%S"),
|
|
**{field: None for field in fields}
|
|
})
|
|
|
|
return data
|
|
|
|
|
|
def get_top_tickers(sector_ticker):
|
|
with open(f"json/etf/holding/{sector_ticker}.json", "r") as file:
|
|
holdings_data = orjson.loads(file.read())
|
|
# Build a dictionary mapping ticker symbols to their weightPercentage.
|
|
data = [item['symbol'] for item in holdings_data['holdings']]
|
|
|
|
res_list = []
|
|
for symbol in data:
|
|
try:
|
|
with open(f"json/options-stats/companies/{symbol}.json","r") as file:
|
|
stats_data = orjson.loads(file.read())
|
|
|
|
new_item = {key: safe_round(value) for key, value in stats_data.items()}
|
|
|
|
with open(f"json/quote/{symbol}.json") as file:
|
|
quote_data = orjson.loads(file.read())
|
|
new_item['symbol'] = symbol
|
|
new_item['name'] = quote_data['name']
|
|
new_item['price'] = round(float(quote_data['price']), 2)
|
|
new_item['changesPercentage'] = round(float(quote_data['changesPercentage']), 2)
|
|
|
|
if new_item['net_premium']:
|
|
res_list.append(new_item)
|
|
except:
|
|
pass
|
|
|
|
# Add rank to each item
|
|
res_list = [item for item in res_list if 'net_call_premium' in item and 'net_put_premium' in item]
|
|
res_list = sorted(res_list, key=lambda item: item['net_premium'], reverse=True)
|
|
|
|
for rank, item in enumerate(res_list, 1):
|
|
item['rank'] = rank
|
|
|
|
return res_list
|
|
|
|
|
|
|
|
def get_market_flow():
|
|
market_tide = get_sector_data(sector_ticker="SPY")
|
|
top_pos_tickers = get_top_tickers(sector_ticker="SPY")
|
|
top_neg_tickers = sorted(get_top_tickers(sector_ticker="SPY"), key=lambda item: item['net_premium'])
|
|
for rank, item in enumerate(top_neg_tickers, 1):
|
|
item['rank'] = rank
|
|
|
|
data = {'marketTide': market_tide, 'topPosNetPremium': top_pos_tickers[:10], 'topNegNetPremium': top_neg_tickers[:10]}
|
|
if data:
|
|
save_json(data, 'overview')
|
|
|
|
|
|
def get_sector_flow():
|
|
sector_dict = {}
|
|
top_pos_tickers_dict = {}
|
|
top_neg_tickers_dict = {}
|
|
|
|
for sector_ticker in ["XLB", "XLC", "XLY", "XLP", "XLE", "XLF", "XLV", "XLI", "XLRE", "XLK", "XLU"]:
|
|
sector_data = get_sector_data(sector_ticker=sector_ticker)
|
|
top_pos_tickers = get_top_tickers(sector_ticker=sector_ticker)
|
|
top_neg_tickers = sorted(top_pos_tickers, key=lambda item: item['net_premium'])
|
|
|
|
for rank, item in enumerate(top_neg_tickers, 1):
|
|
item['rank'] = rank
|
|
|
|
sector_dict[sector_ticker] = sector_data
|
|
top_pos_tickers_dict[sector_ticker] = top_pos_tickers[:10]
|
|
top_neg_tickers_dict[sector_ticker] = top_neg_tickers[:10]
|
|
|
|
|
|
data = {
|
|
'sectorFlow': sector_dict,
|
|
'topPosNetPremium': top_pos_tickers_dict,
|
|
'topNegNetPremium': top_neg_tickers_dict
|
|
}
|
|
|
|
if data:
|
|
save_json(data, 'sector')
|
|
|
|
|
|
def main():
|
|
|
|
get_market_flow()
|
|
get_sector_flow()
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|