286 lines
11 KiB
Python
286 lines
11 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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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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from GetStartEndDate import GetStartEndDate
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from tqdm import tqdm
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import re
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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, filename):
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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}/{filename}.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):
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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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# 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['time']
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# Match with price_list
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for price in price_list:
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if price['time'] == 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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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_sector_data(sector_ticker,interval_1m=True):
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res_list = []
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# Load the options flow data.
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with open("json/options-flow/feed/data.json", "r") as file:
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all_data = orjson.loads(file.read())
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# Load ETF holdings data and extract ticker weights.
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with open(f"json/etf/holding/{sector_ticker}.json", "r") as file:
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holdings_data = orjson.loads(file.read())
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# Build a dictionary mapping ticker symbols to their weightPercentage.
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ticker_weights = {item['symbol']: item['weightPercentage'] for item in holdings_data['holdings']}
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# Use a common dictionary to accumulate flows across all tickers.
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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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# Process each ticker's data using its weight.
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for ticker in tqdm(ticker_weights.keys()):
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# Convert the weight percentage to a fraction.
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weight = 1 #ticker_weights[ticker] / 100.0 #ignore weights of sector
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# Filter data for the current ticker.
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ticker_data = [item for item in all_data if item.get('ticker') == ticker]
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ticker_data.sort(key=lambda x: x['time'])
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for item in ticker_data:
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try:
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# Combine date and time, then truncate seconds and microseconds.
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dt = datetime.strptime(f"{item['date']} {item['time']}", "%Y-%m-%d %H:%M:%S")
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dt = dt.replace(second=0, microsecond=0)
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# Adjust to the start of the minute if using 1-minute intervals.
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if interval_1m:
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minute = dt.minute - (dt.minute % 1)
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dt = dt.replace(minute=minute)
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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", "")
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put_call = item.get("put_call", "")
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vol = int(item.get("volume", 0))
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# Update metrics, scaled by the ticker's weight.
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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 * weight
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delta_data[rounded_ts]['call_ask_vol'] += vol * weight
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_call_premium'] -= cost * weight
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delta_data[rounded_ts]['call_bid_vol'] += vol * weight
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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 * weight
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delta_data[rounded_ts]['put_ask_vol'] += vol * weight
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elif sentiment == "Bearish":
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delta_data[rounded_ts]['cumulative_net_put_premium'] -= cost * weight
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delta_data[rounded_ts]['put_bid_vol'] += vol * weight
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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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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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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']) - (cumulative['put_ask'] - cumulative['put_bid'])
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res_list.append({
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'time': ts,
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'net_call_premium': round(cumulative['net_call_premium']),
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'net_put_premium': round(cumulative['net_put_premium']),
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'call_volume': round(call_volume),
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'put_volume': round(put_volume),
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'net_volume': round(net_volume),
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})
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# Sort the results list by time.
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res_list.sort(key=lambda x: x['time'])
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# Get the price list for the sector ticker.
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price_list = asyncio.run(get_stock_chart_data(sector_ticker))
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if len(price_list) == 0:
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with open(f"json/one-day-price/{sector_ticker}.json", "r") as file:
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price_list = orjson.loads(file.read())
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# Append closing prices to the data.
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data = add_close_to_data(price_list, res_list)
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# Ensure that each minute until the specified end time (e.g., 16:01:00) is present.
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fields = ['net_call_premium', 'net_put_premium', 'call_volume', 'put_volume', 'net_volume', 'close']
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last_time = datetime.strptime(data[-1]['time'], "%Y-%m-%d %H:%M:%S")
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end_time = last_time.replace(hour=16, minute=1, second=0)
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while last_time < end_time:
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last_time += timedelta(minutes=1)
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data.append({
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'time': last_time.strftime("%Y-%m-%d %H:%M:%S"),
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**{field: None for field in fields}
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})
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return data
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def get_top_tickers(sector_ticker):
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with open(f"json/etf/holding/{sector_ticker}.json", "r") as file:
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holdings_data = orjson.loads(file.read())
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# Build a dictionary mapping ticker symbols to their weightPercentage.
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data = [item['symbol'] for item in holdings_data['holdings']]
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res_list = []
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for symbol in data:
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try:
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with open(f"json/options-stats/companies/{symbol}.json","r") as file:
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stats_data = orjson.loads(file.read())
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new_item = {key: safe_round(value) for key, value in stats_data.items()}
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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['symbol'] = symbol
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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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if new_item['net_premium']:
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res_list.append(new_item)
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except:
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pass
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# Add rank to each item
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res_list = [item for item in res_list if 'net_call_premium' in item and 'net_put_premium' in item]
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res_list = sorted(res_list, key=lambda item: item['net_premium'], reverse=True)
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for rank, item in enumerate(res_list, 1):
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item['rank'] = rank
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return res_list
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def get_market_flow():
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market_tide = get_sector_data(sector_ticker="SPY")
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top_pos_tickers = get_top_tickers(sector_ticker="SPY")
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top_neg_tickers = sorted(get_top_tickers(sector_ticker="SPY"), key=lambda item: item['net_premium'])
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for rank, item in enumerate(top_neg_tickers, 1):
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item['rank'] = rank
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data = {'marketTide': market_tide, 'topPosNetPremium': top_pos_tickers[:10], 'topNegNetPremium': top_neg_tickers[:10]}
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if data:
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save_json(data, 'overview')
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def get_sector_flow():
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sector_dict = {}
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top_pos_tickers_dict = {}
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top_neg_tickers_dict = {}
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for sector_ticker in ["XLB", "XLC", "XLY", "XLP", "XLE", "XLF", "XLV", "XLI", "XLRE", "XLK", "XLU"]:
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sector_data = get_sector_data(sector_ticker=sector_ticker)
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top_pos_tickers = get_top_tickers(sector_ticker=sector_ticker)
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top_neg_tickers = sorted(get_top_tickers(sector_ticker=sector_ticker), key=lambda item: item['net_premium'])
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for rank, item in enumerate(top_neg_tickers, 1):
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item['rank'] = rank
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sector_dict[sector_ticker] = sector_data
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top_pos_tickers_dict[sector_ticker] = top_pos_tickers[:10]
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top_neg_tickers_dict[sector_ticker] = top_neg_tickers[:10]
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data = {
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'sectorFlow': sector_dict,
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'topPosNetPremium': top_pos_tickers_dict,
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'topNegNetPremium': top_neg_tickers_dict
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}
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if data:
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save_json(data, 'sector')
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def main():
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get_market_flow()
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get_sector_flow()
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if __name__ == '__main__':
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main()
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