CASH Algo Trading Challenge 2023

About the Challenge

Welcome to CASH Algo Trading Challenge 2023.

Ideate your trading algorithms, test on a robust back-testing platform, and compete to make a name for yourself! Gain professional industry knowledge and practical skills to prepare yourself as the next-gen algo-trading professional.

This Challenge is open to public. Create teams of 1~4 members with diverse skills (maths, statistics, finance, coding, presentation) and register by 22 May 2023.

1st Round: Ideate

Registered teams of 1-4 members will prepare a ppt less than 15 slides consisting of:

Executive summary
Trading idea description
Trading logic
Team biography
Implementation detail

Coding is not required in this round

Submission deadline: 29 May 2023

Result announced: 31 May 2023

2nd Round: Test

Teams will code their trading algorithm and test on ALGOGENE's back-testing engine, then submit the code

Judges will backtest trading algorithms for return, volatility, robustness and practicality to select advancing teams

Submission deadline: 28 Jun 2023

Result announced: 30 Jun 2023

Final Round: Compete

Each team need to compete in out-sample forward test and live paper trading

Live paper trading will last for 2 months from 1-Jul-2023 to 31-Aug-2023

Teams will present trading plan to judges in a 5-minute pitch session on final day

Judges will select winners in each of these categories:

Best Sharpe
Best Market Return
Best Strategy Design

Final Day: 10 Sep 2023

Judging Criteria

Schedule

Date	Event
22 May 2023	Deadline of Registration
29 May 2023	Deadline of 1st Round: Idea and Strategy Proposal Submission
31 May 2023	Announcement of 1st Round Winners
28 Jun 2023	Deadline 2nd Round: Back-testing Completed and Code submission
30 Jun 2023	Announcement of 2nd Round Winners
31 Aug 2023	2-month Live Paper Trading Completed
10 Sep 2023	Final Day and Award Ceremony

Juding Criteria

Judging panel criteria comprises team performance (30%), trading strategy (30%), and presentation (10%), plus the risk-adjusted return (30%) as computed by the ALGOGENE back-test system.

(A) Best Performance

A good return is the primary goal of every algo trading strategy. ALGOGENE's state-of-the-art trading system will back-test the trading strategies of Finale teams to ascertain outcomes. The team with the highest return in one year will win this award.

(B) Best Sharpe

A good strategy does not only need to be profitable, but it is also important to take care of the risk taking. The team with the highest Sharpe will win this award.

(C) Best Strategy Design

Recognises Finale participants' knowledge and skills in developing the best algo trading strategy that demonstrated the following:

Trading Strategy Logic: reasonable return, manageable risk, stability and predictability;
Creativity: innovative use of statistical models, parameters, forecasting methodology or interpretation of data and novel ideas based on existing theory and models; and
Execution Potential: a realistic strategy and full consideration of factors including capital, trading time, market impact and risk management.

Awards

There are 3 award categories: (A) Best Sharpe (B) Best Market Return (C) Best Strategy Design.

The winning team from each category will be entitled to:

Cash prize

Grand prize: US$8,000

Best Return/Sharpe/Strategy Design

1st place: US$1,000 x 3
2nd place: US$600 x 3

Certificate

All prize winners will receive a participation certificate

Free Subscription of Back-testing Engine

All prize winners will get 1-year free subscription of ALGOGENE's back-testing platform

Algo NFT

All prize winners will have a chance to get their algo minted as NFTs in Opensea

Employment Contract

All prize winners may have a chance to receive an employment contract worth US$100,000 from CASH Algo

Join Algo Challenge Association (ACA)

All prize winners will connect with algo fund managers and industrial professionals in Algo Challenge Association (ACA)

Rules & Regulations

The Challenge is open to public.
Each individual contestant is only allowed to be in one team.
Each team must elect one representative to liaise with the organizers.
All entries must be the original work of the contestant team. Any form of plagiarism is prohibited and the contestant team will be disqualified from the Challenge.
Each individual contestant is required to sign a Non-Disclosure Agreement with the organizers.
Finalists must present valid Student ID cards and provide a copy for verification.
The judging panel’s decision of the Challenge results is final. The organizers reserve the right to the final decision in all other areas.
An online briefing session will be arranged during registration period to answer any inquiries about the Challenge.

FAQ

Q: If a person is in overseas, can he still join the competition via zoom?

A: Overseas participants are still able to join the Finale via Zoom, though the Finale will be held phyiscally in Hong Kong. The Finale and award presentation is on 10 Sep 2023.

Resources

The dataset provided includes the historical trading records of Hong Kong equity market in past years. Kindly note that data provided is strictly for trading strategy prototyping purposes only. Final round evaluations will be carried out using a new data set with the same structure but from a different time period. Thus, over-fitting of the data set is discouraged.

Trading Products

HK Large Cap
ETF

Datasets

Provide access to required historical data for model development and testing

Market data
News
Economic Statistics
Corporate data
Share holding

System

Access to ALGOGENE's back-testing engine, cloud simulation environment and relevant technical documents

Results

The final result of the CASH Algo Trading Challenge 2023/24 comes out! Congratulation to all winning teams!

Best Return: live trading period 01/07/2023-31/08/2023

Best Sharpe: 1-year out-sample forward test 1/02/2022 - 31/05/2022

Rank	Team	Best Return
1	ACBH	30.87%
2	MASS	28.68%

Rank	Team	Best Sharpe
1	ACBH	3.39
2	MASS	3.05

Rank	Team	Best Strategy Design
1	University Street Capital Management	Low and Medium Frequency Trading Algorithm Based on Deep Learning Model and R-Breaker Model. It will predict by combining the two model and dynamically update their weight.
2	Spice & Fries	Our trading strategy focuses on ranking HSI stocks using the Generalized Risk-Adjusted Ratio, where risks are dynamically adjusted during different periods. We then apply Reverse MACD strategy to allocate buy/sell signals to stocks with the highest ranking.

Team	Grand Prize
MASS	Hybrid of mean-reversion, momentum, trend following, dual moving average crossover and key and relevant stock pairs.

Media Coverage

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