Principles of Algorithmic Options Trading

Learn how moving averages smooth price noise, reveal trend crossovers for entry and exit signals, and apply intraday and exponential moving averages to options trading.

Discover how volume drives day trading trends in technical analysis, as institutional investors trade in large volumes, with volume often preceding price movement and signaling buying or selling pressure.

Learn VWAP, calculated as the cumulative close times volume divided by cumulative volume, an intraday indicator blending volume and price to signal moves around VWAP and its bands.

Anchored VAP recalibrates the VAP bands after a sudden price move or volume spike, anchoring to that moment to improve bounce predictions and refine algorithmic entries and exits.

Explore basic fundamental analysis in stock trading, not the course focus. Review market cap, earnings, eps, p/e, p/b, ownership, analyst ratings, and sentiment that may drive moves.

Buy puts give the right to sell underlying asset when a bearish move is expected, with premium and intrinsic and extrinsic value similar to calls, and set a loss limit.

Learn how to use the robin_stocks Python library to access Robinhood, TD Ameritrade, and Gemini, install via pip, and implement programmatic two factor authentication with pyotp and environment variables.

Sign up for developer access, install the unofficial TD Ameritrade Python API via pip, log in with the TD client, and use Thinkorswim for real-time data.

Learn how to manage login attempts, use pickle files to store authentication across sessions, handle expiration, and dynamically recreate credentials with multifactor authentication to keep algorithmic trading running.

Obtain real-time option pricing data driven by stock price, time to expiration, and implied volatility. Learn to fetch the option premium and place orders using TD Ameritrade and robin stocks.

Automate your trading bot with shell scripting to start at 920 every morning and stop at market close, using cron or task scheduler across macOS, Linux, and Windows.

Maintain source code with version control and unit, functional, and regression testing; commit well-commented changes using Git or similar tools, and create feature branches to backtest and deploy trading strategies.

Ensure data robustness by validating real-time data against TD Ameritrade, aligning VWAP values, standard deviation bands, on-balance volume, candlestick patterns, and RSI, and correcting discrepancies to improve backtesting and simulation.

Simulate options trading to test algorithms in simulated mode for 2-3 months, observe entry timing via candlestick patterns and time of day, and exit conservatively using predefined rules.

Explore RSI and MACD strategy for multi-day option trades, emphasizing ticker screening and persistently storing trade data to resume, close profitable options, and delete records after exit.

Algorithmically check for recent stock upgrades and downgrades using analyst ratings from Yahoo, Google, or TipRanks, parsing data with requests and BeautifulSoup, and tracking the recommendation trend in real time.

Monitor market volatility with the VIX and related indices to guide longer-term options, using CBOE data, VCN, CNN fear and greed, and online sources for algorithmic timing.

Combine candlesticks with volume weighted average price bands for day trading, comparing one-minute and five-minute charts while managing noise and pullbacks; discuss API call limits, data accuracy, and data sources.

Enter when candlesticks pierce VWAP with a red candle and manage with a trailing stop; exit on bounces off plus or minus one standard deviation VWAP levels and re-enter opportunities.