/Quantitative-finance

Derivatives valuation, random processes, time-series forecasting

Primary LanguageJupyter Notebook

Quant

0. Random processes

1. Options Valuation

a. Black and Scholes world notebook

  • European vanilla call option pricing also in k\q
  • Monte Carlo simulation
  • Implied volatility surface (smile) calculation
  • AAPL option chain processing from CBOE

2. Time series analysis & (volatility) forecasting

a. Apple Inc (AAPL) stock price notebook

  • Financial returns analysis
  • Classical forecasting methods (AR, MA, ARIMA, GARCH)

b. Bayesian ARMA model report

c. Hybrid (classical + deep learning) forecasting methods

3. Data and retrivial

a. WebSocket Poloniex BTC/ETH feed handler that saves data to flat files, MySQL or kdb+/q database also in k\q

b. Apple Inc. (AAPL) option chain from CBOE on 14th Nov 2018

c. Bloomberg API script that retrives price history of SPX index members

d. Interactive brokers API script that retrives historical prices

e. Wharton Research Data Services script for retriving and saving TAQ data