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Complete Walkthrough Code For Options Price Forecast
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| import requests | |
| import numpy as np | |
| import pandas as pd | |
| from datetime import datetime, date | |
| api_key = "YOUR_INTRINIO_API_KEY_HERE" | |
| def _latest_stock_price(identifier, source="intrinio_mx"): | |
| res = requests.get(f"https://api-v2.intrinio.com/securities/{identifier}/prices/realtime?source={source}&api_key={api_key}").json() | |
| latest_stock_price = res.get("last_price") | |
| return latest_stock_price | |
| def _option_expirations_list(identifier, after_date=date.today()): | |
| res = requests.get(f"https://api-v2.intrinio.com/options/expirations/{identifier}?after={after_date}&api_key={api_key}").json() | |
| expirations_list = list(res.get("expirations")) | |
| return expirations_list[::-1] | |
| def _option_strike_price_and_implied_volatility(identifier, option_expiration_date): | |
| latest_stock_price = _latest_stock_price(identifier) | |
| option_strike_price, implied_volatility = None, None | |
| try: | |
| res = requests.get(f"https://api-v2.intrinio.com/options/chain/{identifier}/{option_expiration_date}/realtime", params = { | |
| "source": "delayed", | |
| "type": "call", | |
| "api_key": api_key, | |
| }).json() | |
| # Create Options DF | |
| options_chain = res.get("chain") | |
| options_dataset = pd.json_normalize(options_chain) | |
| # Filter For Near The Money Option Strike Info | |
| near_the_money_option = options_dataset.iloc[(options_dataset["option.strike"]-latest_stock_price).abs().argsort()[:2]].iloc[0] | |
| # Grab Required Stats | |
| implied_volatility = near_the_money_option["stats.implied_volatility"] | |
| option_strike_price = near_the_money_option["option.strike"] | |
| return option_strike_price, implied_volatility | |
| except KeyError: | |
| return option_strike_price, implied_volatility | |
| def _stock_standard_deviation_range(option_strike_price, implied_volatility, option_expiration_date): | |
| upper_range, lower_range = None, None | |
| if option_strike_price or implied_volatility != None: | |
| days_in_year = 365 | |
| option_expiration_datetime = datetime.strptime(option_expiration_date, "%Y-%m-%d").date() | |
| days_until_expiration = (option_expiration_datetime - date.today()).days | |
| days_expo_ratio = days_until_expiration / days_in_year | |
| days_expo_ratio_sqrt = np.sqrt(days_expo_ratio) | |
| std_dev_price_range_by_expo = option_strike_price * implied_volatility * days_expo_ratio_sqrt | |
| std_dev_price_range_by_expo = round(std_dev_price_range_by_expo, 2) | |
| upper_range = round((option_strike_price + std_dev_price_range_by_expo), 2) | |
| lower_range = round((option_strike_price - std_dev_price_range_by_expo), 2) | |
| return upper_range, lower_range | |
| def _options_forecast_dataset(identifier): | |
| options_forecast_data_list = [] | |
| expirations_list = _option_expirations_list(identifier) | |
| for expiration_date in expirations_list: | |
| option_strike_price, implied_volatility = _option_strike_price_and_implied_volatility(identifier, expiration_date) | |
| upper_range, lower_range = _stock_standard_deviation_range(option_strike_price, implied_volatility, expiration_date) | |
| if upper_range and lower_range != None: | |
| options_forecast_data_list.append({ | |
| "expiration": expiration_date, | |
| "upper_range": upper_range, | |
| "lower_range": lower_range, | |
| }) | |
| options_forecast_dataset = pd.DataFrame(options_forecast_data_list) | |
| options_forecast_dataset = options_forecast_dataset.loc[ | |
| (options_forecast_dataset["upper_range"] >= 0 ) & (options_forecast_dataset["lower_range"] >= 0)] | |
| return options_forecast_dataset | |
| options_forecast_dataset = _options_forecast_dataset("AAPL") | |
| options_forecast_dataset = _options_forecast_dataset("TSLA") | |
| # Latest Price for AAPL on 2022-04-08 is $169.95 | |
| # 0 2022-04-14 176.29 163.71 | |
| # 1 2022-04-22 178.93 161.07 | |
| # 2 2022-04-29 182.96 157.04 | |
| # 3 2022-05-06 184.67 155.33 | |
| # 4 2022-05-13 186.01 153.99 | |
| # 5 2022-05-20 187.47 152.53 | |
| # 6 2022-05-27 188.88 151.12 | |
| # 7 2022-06-17 192.46 147.54 | |
| # 8 2022-07-15 196.44 143.56 | |
| # 9 2022-08-19 202.22 137.78 | |
| # 10 2022-09-16 205.46 134.54 | |
| # 11 2022-10-21 209.48 130.52 | |
| # 12 2022-11-18 212.83 127.17 | |
| # 13 2023-01-20 218.54 121.46 | |
| # 14 2023-03-17 224.56 115.44 | |
| # 15 2023-06-16 233.06 106.94 | |
| # 16 2023-09-15 241.05 98.95 | |
| # 17 2024-01-19 249.91 90.09 | |
| # 18 2024-06-21 261.83 78.17 | |
| # Latest Price for TSLA on 2022-04-08 is $1,032.55 | |
| # expiration upper_range lower_range | |
| # 0 2022-04-14 1106.94 963.06 | |
| # 1 2022-04-22 1165.11 904.89 | |
| # 2 2022-04-29 1187.58 882.42 | |
| # 3 2022-05-06 1208.82 861.18 | |
| # 4 2022-05-13 1226.35 843.65 | |
| # 5 2022-05-20 1231.18 818.82 | |
| # 6 2022-05-27 1257.07 802.93 | |
| # 7 2022-06-17 1287.11 762.89 | |
| # 8 2022-07-15 1335.09 714.91 | |
| # 9 2022-08-19 1427.98 672.02 | |
| # 10 2022-09-16 1445.61 624.39 | |
| # 11 2022-10-21 1513.49 586.51 | |
| # 12 2022-11-18 1550.70 549.30 | |
| # 13 2022-12-16 1586.13 513.87 | |
| # 14 2023-01-20 1587.55 462.45 | |
| # 15 2023-03-17 1685.35 414.65 | |
| # 16 2023-06-16 1770.44 329.56 | |
| # 17 2023-09-15 1807.89 242.11 | |
| # 18 2024-01-19 1908.67 151.33 | |
| # 19 2024-06-21 2020.27 39.73 |
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