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QC Quantitative Developer.
MSc Financial Engineering in WQU with "excellent" grades in all courses.
Pure mathematics enthusiast.
Arbitrage reigns supreme!

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.

-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

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

-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 Upcoming Holiday Momentum for Amazon

Hi Decebal, you can click on the hyperlinks in the text

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 submitted the research Factor Sector Rotation with Kavout

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

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

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

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...

Louis left a comment in the discussion Upcoming Holiday Momentum for Amazon

Hi Decebal, you can click on the hyperlinks in the text

3 days ago