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.

Explore candlestick charts with simple moving averages and exponential moving averages to identify day trading signals, using one-minute candles, open-high-low-close data, and crossovers to time long and short exits.

Study candlestick charts with red and green candles and wicks, based on open, high, low, and close data, and see how moving averages with hike and ASHI candles improve trends.

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.

Explore on balance volume, a cumulative momentum indicator that blends volume and price movement by adding or subtracting volume based on candle direction, which correlates with price movement.

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.

Apply VWAP standard deviation bands to intraday trading by calculating one-, two-, and three-sigma bands around VWAP using volume-weighted data. Use dynamic support and resistance to anticipate bounces and reversals.

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.

Learn how RSI and MACD momentum indicators, with 3/6/9 EMAs, guide day trading through crossovers and oversold/overbought signals, illustrated by a Tesla example and automated algorithm decisions.

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.

Explore stock screening setups for options trading, using short-term moves, average true range volatility, RSI oversold/overbought, volume and relative volume filters, and automated ticker switching.

Discuss the fundamentals of options trading, contrast with stock ownership, explain premium, rights to buy or sell, expiration dates, and 100-share contract sizes.

Learn buy call options: pay a premium for the right to buy 100 shares per contract by expiration, with intrinsic and extrinsic values shaping risk and time decay.

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 selling a covered call obligates sale of 100 shares at a chosen strike, collects a premium, and allows profit while managing risk before expiration.

Learn how selling put options creates an obligation to buy a stock at the strike price, earning a premium while requiring collateral when prices fall.

Discover basic and advanced option strategies, including calendar spreads, credit and debit spreads, iron condor, together with straddles and diagonal spreads, with a focus on risk management in algorithmic trading.

Explore option greeks—delta, gamma, theta, vega, and rho (roe)—and how delta guides automated strike selection by linking option premium to stock price, expiration, and probability.

Set up a python-based environment for algorithmic options trading, using libraries like pandas, numpy, matplotlib, Tulip, and requests, with real-time data from TD Ameritrade or Robinhood.

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.

Explore the TD Ameritrade Thinkorswim platform to analyze charts with one-minute candles, volume, and versatile studies like VAP, unbalanced volume, and moving averages to develop personal algorithmic trading rules.

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.

Acquire real-time market data for algorithmic trading in Python, using live prices and bid-ask data to inform entry and exit decisions, with exception handling and redundancy across Robinhood and Thinkorswim.

Automate option expiration dates using a simple keyword-driven approach that handles weekly and monthly expirations, holidays, and edge cases with a trading calendar.

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.

Learn to design robust exception handling for automated options trading by creating fallback data retrieval paths and graceful termination to keep algorithms running.

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.

Master real-time option order placement by rounding premiums to the proper precision, selecting near-the-money strikes, and using timely cancellations in volatile markets.

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.

Use a VWAP-based stock screener with two standard deviation bands to pick options-ready stocks, filtering downgrades and watching candlestick signals near the bands.

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.

Apply RSI and MACD rules with a 40 threshold and MACD cross, using a rolling average to reduce noise, and set one-month expiration with 50% trail stops.

Apply RSI and MACD signals to day trade using threshold crossings and crossovers to enter or exit. Recognize how subjective levels affect entries, exits, and automation.

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.

Trade during the open 9:30–10:30 am ET and near the close 3:30–4:00 pm using obv to enter, pivot when needed, and ride with trailing stops and vwap bands.

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.

Learn to trade with one‑ and five‑minute candlesticks, using VWAP and VP levels, unbalanced volume, and exhausted candles to time entries, exits, and trailing stops.

Combine candlestick analysis with OBV smoothed by seven and ten ema to generate entry and exit signals, using the gap between the EMAs and a trail stop, illustrated on Tesla five minute candles.

Set up rules, back test thoroughly, and simulate under realistic conditions. Use candlestick analysis with VAP levels and OBV, verify data, start small, set trail stop loss, and keep learning.