INTRODUCTION TO FUTURES (FORWARD) AND OPTIONS MARKETS
(FINE 3006)
Individual term project assignment (maximum score 50):
Title: The performance of an algorithmic trading strategy (or trading strategies) with futures contracts involve a forecast of futures price (a single futures contract) or the spread between different futures contracts (calendar spread: e.g., buy the spot month short the next month, inter-commodity spread: e.g., more bullish for china than for hong kong; buy the futures on A50 ETF on SGX and short hang seng index futures). For performance measures: for table format and measurements (simple monthly/annualized average return, volatility, sharpe ratio, success to failure ratio #right/#right+wrong, semi-variance, take away the positive or negative number in calculating the volatility) see for example Che and Fung 2006 journal of futures market / Fung and Che Hong Kong Monetary Authority working paper series for table format and measurement of performance.
1. The percentage of times your model indicates a correct / incorrect trade (whether your prediction of market direction is correct for directional trading)
2. The risk and return of the strategy: use summary statistics to describe the distribution of the payoff or returns.
Example (e.g., Hang Seng Index futures S&P 500 index futures, FTSE A50 ETF futures contracts (SGX), crude oil futures contracts, US T-bond futures contract, US T-bond futures contracts, China bond futures contracts, gold futures contract, CSI 300 contract, CAC50, DAX 30 and etc.
Strategies: day trading a single contract or day trade a spread, overnight trade (1-day exposure) start the position today and close out tmr, spread trade: long spot month short next month or vice versa, intercommodity trade: long H index futures short hang seng index, buy csi short S&P 500/100, russel 2000, ftse 100, nikkei 225, DAX 30
Need to know the contract specification including settlement features.
Use at least 10-years of intradaily/daily data/weekly/monthly which provides at a minimum of 120 (monthly) observations.
I strongly encourage you to download data from Bloomberg to show to your future employer you are Bloomberg literate, take a number of Bloomberg test at home, Bloomberg literacy tests, BAT = Bloomberg attitude test. Bloomberg literacy.
Estimation period: use first 5 year data (learning period) to construct your model, quantitatively mean square, prob of a correct prediction (up or down, or flat). Your end date of your data set should be between December 2010 and Jan 2021 (or beyond). 5 years to train your model 5 years to test the performance, 6-year training, 4 year to test the performance; if you trade once a month, then 4 year means 48 observe. Test statistical and economic performance via risk and return analyses.
5-years passed, then you use your model to make a trade for the next month,
- Recalibrate your model parameters with each additional monthly data afterward
- Rolling window that keeps 5 years of data, increase the number of observations in your model up until 1-month before end of data period.
- Stay with the same model and parameters, and use the other 5-year to test the statistical accuracy P/L distribution of the model
Estimate the distribution potential economic profit.
The data may contain the followings:
Valuation indicators: P/E ratio of the underlying asset, P/B, D/P, E/P on the asset (e.g., hang seng index, csi300 index, A50ETF NAV)
Technical indicators: momentum measures, RSI, Bollinger tunnel, moving averages, signals short MA crosses long MA, chartist strategies
Approaches: technical forecasts, contingency tables, regression analysis, machine learning (artificial intelligence)
Macroeconomic informations: unemployment, fiscal deficit, interest rate, change in money supply
Keywords: trading strategies with futures, momentum, forecast of expiration day settlement price
Need to know the contract specification.
Need to know the contract specification including settlement features (physical versus cash settlement).
Use at least 10-years of intradaily and/or daily data which provides at a maximum 120 expiration day events.
The data may contain the followings:
Open interest, volume, margin requirement, distribution (trader identity) of large positions, and so on.
Valuation indicators: P/E ratio of the underlying asset
Put call ratio, put/call price ratio (price is in terms of implied volatility) price is proportional to implied volatility,
Put implied volatility/call implied volatility
Approaches: technical forecasts / technical trading rules /chartist, contingency tables, regression analysis, machine learning (artificial intelligence)
Number of pages: abstract/executive summary (1/2 – 1 page) objective and motivation statement (1 page), your story on the logic behind your algorithm (1-2 pages) and the literature review (2-3 pages), data and methodology (2-3 pages), summary and interpretation of your findings (2-3 pages), tables of results (4-5 pages of tables), conclusion (1/2 page).
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