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Last active June 29, 2023 07:28
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Complete Walkthrough Code For Options Price Forecast
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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