Louis Szeto
Quantitative Developer
Date Joined
1st May, 2020
Bootcamp progress
97% Complete
Achievements
Algorithm Sharing
Share an algorithm with the community by making your algorithm public.
2021-03-31 15:27:48
Chatter Box
Leave at least 100 comments on community posts.
2021-07-02 05:52:16
Backtester
Run at least 100 backtests on QuantConnect's cloud.
2020-05-04 15:14:35
Live Trading
Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2020-05-19 15:06:47
Parallel Live Trading
Run multiple live algorithms simultaneously.
2021-06-17 00:04:33
Cloner
Clone at least 100 algorithms from the community.
2021-07-10 17:39:39
C# Programming
Choose C# as your preferred programming language.
2021-06-07 01:35:17
Python Programming
Choose Python as your preferred programming language.
2020-05-01 15:37:47
Popular
Receive at least 10 upvotes from different users in the community.
2021-06-15 06:36:18
Staff
Become a member of the QuantConnect team.
2021-08-12 02:31:53
Bug Hunter
Report at least 10 bugs to QuantConnect.
2021-03-08 01:43:34
Community Editor
Granted when you earn community moderation privileges.
2022-05-25 16:04:12
Open Source Contributor
Contribute to the QuantConnect open source project on GitHub.
2021-06-15 01:22:28
Boot Camp Private
Complete a beginner bootcamp in the algorithm lab.
2021-06-11 13:23:55
Boot Camp Corporal
Complete an advanced bootcamp in the algorithm lab.
2021-06-17 08:19:24
Verified User
Verify your identity and become a verified QuantConnect user.
2021-08-12 02:28:10
Alpha Streams Developer
Have at least 1 Alpha Stream submission approved.
2020-08-21 16:37:03
Alpha Streams Artisan
Have at least 3 Alpha Stream submissions approved.
2020-09-02 01:50:03
Outstanding Community Service
Awarded by administrators for outstanding voluntary support of other community members.
2021-06-15 00:10:59
Connected with Discord
2023-02-01 03:13:52
- Profile
- Backtests
- Community
Biography
Activity on QuantConnect
We are pioneering the radical future for open-source quant finance. QuantConnect is the world's largest quant community, empowering 220,000 quants with a framework, data, and infrastructure for their investments.
Public Backtests (222)
View More-99.982Net Profit
0PSR
-2.544Sharpe Ratio
-0.92Alpha
-0.182Beta
-95.909CAR
100Drawdown
97Loss Rate
0Parameters
1Security Types
-1.84Sortino Ratio
674Tradeable Dates
93143Trades
5.066Treynor Ratio
3Win Rate
-19.367Net Profit
1.249PSR
-0.317Sharpe Ratio
-0.067Alpha
-0.169Beta
-7.685CAR
29Drawdown
59Loss Rate
0Parameters
1Security Types
-0.414Sortino Ratio
674Tradeable Dates
2693Trades
0.417Treynor Ratio
41Win Rate
29.008Net Profit
12.72PSR
0.243Sharpe Ratio
0.051Alpha
-0.093Beta
9.923CAR
28.6Drawdown
53Loss Rate
0Parameters
1Security Types
0.322Sortino Ratio
674Tradeable Dates
2632Trades
-0.531Treynor Ratio
47Win Rate
81.742Net Profit
35.907PSR
0.915Sharpe Ratio
-0.18Alpha
5.834Beta
49.678CAR
56.9Drawdown
57Loss Rate
0Parameters
1Security Types
1.057Sortino Ratio
460Tradeable Dates
557Trades
0.111Treynor Ratio
43Win Rate
99.04Net Profit
71.297PSR
0.999Sharpe Ratio
0.085Alpha
0.023Beta
15.971CAR
9.6Drawdown
30Loss Rate
0Parameters
1Security Types
1.184Sortino Ratio
1168Tradeable Dates
1033Trades
3.751Treynor Ratio
70Win Rate
Community
View MoreLouis left a comment in the discussion Can't find self.rsv (Rogers Satchell volatility) in Python
Hi FemtoTrader
Louis left a comment in the discussion System.OutOfMemoryException
Hi Dharmesh
Louis left a comment in the discussion UniversalSelection
Hi Dharmesh
Louis left a comment in the discussion Set Alpha on specific time or on consolidator
Hi
-99.982Net Profit
0PSR
-2.544Sharpe Ratio
-0.92Alpha
-0.182Beta
-95.909CAR
100Drawdown
97Loss Rate
0Parameters
1Security Types
-1.84Sortino Ratio
674Tradeable Dates
93143Trades
5.066Treynor Ratio
3Win Rate
-19.367Net Profit
1.249PSR
-0.317Sharpe Ratio
-0.067Alpha
-0.169Beta
-7.685CAR
29Drawdown
59Loss Rate
0Parameters
1Security Types
-0.414Sortino Ratio
674Tradeable Dates
2693Trades
0.417Treynor Ratio
41Win Rate
29.008Net Profit
12.72PSR
0.243Sharpe Ratio
0.051Alpha
-0.093Beta
9.923CAR
28.6Drawdown
53Loss Rate
0Parameters
1Security Types
0.322Sortino Ratio
674Tradeable Dates
2632Trades
-0.531Treynor Ratio
47Win Rate
81.742Net Profit
35.907PSR
0.915Sharpe Ratio
-0.18Alpha
5.834Beta
49.678CAR
56.9Drawdown
57Loss Rate
0Parameters
1Security Types
1.057Sortino Ratio
460Tradeable Dates
557Trades
0.111Treynor Ratio
43Win Rate
99.04Net Profit
71.297PSR
0.999Sharpe Ratio
0.085Alpha
0.023Beta
15.971CAR
9.6Drawdown
30Loss Rate
0Parameters
1Security Types
1.184Sortino Ratio
1168Tradeable Dates
1033Trades
3.751Treynor Ratio
70Win Rate
100.335Net Profit
94.259PSR
1.996Sharpe Ratio
0.134Alpha
1.207Beta
52.61CAR
10.8Drawdown
33Loss Rate
0Parameters
1Security Types
2.653Sortino Ratio
0Tradeable Dates
2654Trades
0.25Treynor Ratio
67Win Rate
88.154Net Profit
83.969PSR
1.679Sharpe Ratio
0.07Alpha
1.727Beta
52.867CAR
17.9Drawdown
20Loss Rate
0Parameters
1Security Types
2.09Sortino Ratio
0Tradeable Dates
550Trades
0.18Treynor Ratio
80Win Rate
18.247Net Profit
57.612PSR
1.055Sharpe Ratio
0.129Alpha
0.14Beta
18.32CAR
6Drawdown
-0.37Loss Rate
0Parameters
0Security Types
0Tradeable Dates
781Trades
0.794Treynor Ratio
0.42Win Rate
38.046Net Profit
83.646PSR
1.224Sharpe Ratio
-0.005Alpha
0.861Beta
24.219CAR
9.4Drawdown
8Loss Rate
0Parameters
1Security Types
1.527Sortino Ratio
0Tradeable Dates
235Trades
0.131Treynor Ratio
92Win Rate
-23.636Net Profit
0.009PSR
-2.142Sharpe Ratio
-0.11Alpha
-0.582Beta
-16.423CAR
25.8Drawdown
57Loss Rate
0Parameters
1Security Types
-2.896Sortino Ratio
0Tradeable Dates
6598Trades
0.288Treynor Ratio
43Win Rate
24.606Net Profit
40.216PSR
0.484Sharpe Ratio
-0.078Alpha
1.126Beta
16.27CAR
13.9Drawdown
51Loss Rate
0Parameters
1Security Types
0.569Sortino Ratio
453Tradeable Dates
359Trades
0.059Treynor Ratio
49Win Rate
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
0Parameters
2Security Types
14Tradeable Dates
Louis left a comment in the discussion Devcontainer + Cloud hybrid workflow?
Hi Kevin
Louis left a comment in the discussion Can't find self.rsv (Rogers Satchell volatility) in Python
Hi FemtoTrader
Louis left a comment in the discussion System.OutOfMemoryException
Hi Dharmesh
Louis left a comment in the discussion UniversalSelection
Hi Dharmesh
Louis left a comment in the discussion Set Alpha on specific time or on consolidator
Hi
Louis left a comment in the discussion Order rejected on Daily Resolution
Hi Sukhvinder
Louis submitted the research Factor Sector Rotation with Kavout
Abstract
This micro-study examines a factor-driven sector rotation strategy using the Kavout Factor Bundle dataset to identify sector-specific opportunities. By combining factor scores with sector-level analysis, the strategy dynamically allocates portfolios based on sector-specific factor scores. It calculates these scores by summing and normalizing factor scores of stocks within each sector. Capital is allocated proportionally to these scores, ensuring diversification by distributing it equally among constituent stocks. The strategy undergoes monthly rebalancing, focusing on the top 50 US stocks by market capitalization to ensure liquidity and reliability. From March 2023 to August 2024, the strategy achieved a Sharpe ratio of 1.224 and a strong compounded annual return, demonstrating its effectiveness.
Louis submitted the research Intraday Application of Hidden Markov Models
Abstract
This study explores a 3-component Hidden Markov Model (HMM) strategy to detect market regimes and generate buy signals for the top 10 market capitalization stocks. HMMs, effective in identifying unobservable market states, analyze 5-minute close price returns to anticipate market behavior. Large-cap stocks are chosen for their liquidity and market representation. Using a 5-minute bar timeframe, the strategy adapts to intraday fluctuations, capturing short-term opportunities. Implemented in QuantConnect, the strategy shows a Sharpe Ratio of 1.7, Information Ratio of 0.912, Compounding Annual Return of 36.237%, Alpha of 6.9%, and a maximum drawdown of 7.3%, highlighting its ability to identify and capitalize on market regime shifts.
Louis submitted the research Prediction on Futures Contango
Abstract
This discussion focuses on predicting futures contango and investing with a mean-reversion strategy. Contango occurs when the spot prices of further-term contracts are higher than those of nearer-term contracts. The strategy aims to profit from predicting reversion in gold futures contracts expiring within 90 days. The strategy yielded a positive return with a 0.88 Sharpe Ratio over a three-year period. Many things could be done to improve the strategy including; a contango probability and size estimate, an early exit handler, improving the signal for price/contango prediction, and constructing a portfolio of many contracts.
Louis submitted the research Piotroski F-Score Investing
Abstract
The Piotroski F-Score is a tool developed by Joseph Piotroski to measure the financial strength of a company. It consists of 3 categories and 9 sub-scores, with a higher score indicating a stronger financial position. This score is commonly used to filter out undervalued stocks. In this study, the authors hypothesized that companies with higher F-scores would have higher stock prices and returns, as well as better resilience during market downturns. They compared the performance of a portfolio constructed using the F-Score filtering method to the SPY benchmark over a 3-year period. The results showed that the F-Score strategy outperformed the benchmark in terms of total return, compounded annual return, Sharpe Ratio, and information ratio. This suggests that the F-Score is a valuable tool for identifying undervalued stocks and implementing a successful investment strategy.
Louis started the discussion USTUSD is not found
Hi
Louis started the discussion Historical options data in backtest
Hi All
Louis started the discussion Mean CVaR Portfolio Construction Model
Hi everyone
Louis started the discussion Multiple project optimization
I know it sounded a bit weird, but is there any ways to build different projects and create a...
QuantConnect™ 2024. All Rights Reserved
Louis left a comment in the discussion Devcontainer + Cloud hybrid workflow?
Hi Kevin
8 days ago