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Master in statistics at UW. Passionate about algorithm trading.

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.

9Parameters

1Security Types

0Tradeable Dates

39.804Net Profit

0.354Sharpe Ratio

0.02Alpha

0.156Beta

3.526CAR

18.3Drawdown

50Loss Rate

10Parameters

1Security Types

0.0699Sortino Ratio

0Tradeable Dates

1232Trades

0.255Treynor Ratio

50Win Rate

10Parameters

1Security Types

0Tradeable Dates

-7.386Net Profit

-0.061Sharpe Ratio

-0.003Alpha

-0.004Beta

-0.716CAR

24Drawdown

55Loss Rate

9Parameters

1Security Types

-0.0278Sortino Ratio

3303Tradeable Dates

686Trades

0.96Treynor Ratio

45Win Rate

4Parameters

1Security Types

168Tradeable Dates

Daniel submitted the research Fama French Five Factors

The Fama French five-factor model is a widely used financial model that quantifies risk and estimates the expected return on equity. It builds upon the dividend discount model and incorporates five factors: market return, size, value, profitability, and investment pattern. This model is used to develop a stock-picking strategy that focuses on these factors. The strategy involves running a Coarse Selection to filter out equities with no fundamental data or low prices, and then selecting those with the highest dollar volume. The portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. The strategy can be improved by adjusting the fundamental factors, factor weights, and rebalancing frequency.

Daniel submitted the research Seasonality Effect Based On Same Calendar Month Returns

This tutorial discusses the implementation of a seasonality strategy based on historical same-calendar-month returns. The strategy is derived from a research paper on return seasonalities. Seasonality effects in algorithmic trading have been well-documented across various countries, stock returns, and portfolios. The strategy involves selecting the top 100 liquid securities with a price greater than $5 as the universe. For each security, the monthly return for the same-calendar month of the previous year is calculated. Long positions are taken on securities with high monthly returns, while short positions are taken on securities with low monthly returns. The algorithm is rebalanced and the strategy is repeated at the end of each month. The implementation achieves a Sharpe ratio of 0.128 relative to the S&P 500 over the past 10 years. Possible improvements include using multiple years of same-calendar-month returns, incorporating time effects in the returns, and using different criteria for initial universe selection.

Daniel submitted the research Risk Premia In Forex Markets

This tutorial discusses a risk premia strategy in Forex markets based on a skewness indicator. The strategy is derived from a paper that explores the relationship between risk premia and skewness. The tutorial outlines the method for selecting the Forex universe and provides backtest results. The results show a low annual return and suggest potential improvements such as diversifying the Forex universe, adjusting the thresholds for positions, and increasing the length of historical data. The community is encouraged to further develop and test this strategy with different symbols, thresholds, and historical data lengths. The reference to the paper is provided for further reading.

Daniel submitted the research Price And Earnings Momentum

This tutorial discusses a strategy based on the price and earnings momentum effect of stocks, derived from the paper "Momentum" by N. Jegadeesh and S. Titman. Price/return momentum refers to the tendency for stocks that perform well over a three to twelve month period to continue performing well, while stocks that perform poorly tend to continue performing poorly. Earnings momentum refers to the tendency for stocks with high earnings per share (EPS) to outperform stocks with low EPS. The tutorial outlines a quarterly-rebalanced stock strategy based on these momentum factors. The method involves selecting a coarse universe of assets based on volume, price, and fundamental data, and later applying a fine universe filter. The hope is that the community can further develop strategies based on these techniques.

Daniel started the discussion Forex Monday CCI Trend Strategy

Hi all, I would love to share an algorithm with you guys here! This algorithm, Forex Monday CCI...

9Parameters

1Security Types

0Tradeable Dates

39.804Net Profit

0.354Sharpe Ratio

0.02Alpha

0.156Beta

3.526CAR

18.3Drawdown

50Loss Rate

10Parameters

1Security Types

0.0699Sortino Ratio

0Tradeable Dates

1232Trades

0.255Treynor Ratio

50Win Rate

10Parameters

1Security Types

0Tradeable Dates

-7.386Net Profit

-0.061Sharpe Ratio

-0.003Alpha

-0.004Beta

-0.716CAR

24Drawdown

55Loss Rate

9Parameters

1Security Types

-0.0278Sortino Ratio

3303Tradeable Dates

686Trades

0.96Treynor Ratio

45Win Rate

4Parameters

1Security Types

168Tradeable Dates

-3.766Net Profit

-0.763Sharpe Ratio

-0.027Alpha

-0.063Beta

-5.581CAR

5.5Drawdown

50Loss Rate

9Parameters

1Security Types

-0.1927Sortino Ratio

208Tradeable Dates

102Trades

0.607Treynor Ratio

50Win Rate

-0.478Net Profit

-0.08Sharpe Ratio

-0.005Alpha

-0.002Beta

-0.942CAR

6.1Drawdown

56Loss Rate

3Parameters

1Security Types

-0.0456Sortino Ratio

0Tradeable Dates

35Trades

3.359Treynor Ratio

44Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

2Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

63.778Net Profit

0.332Sharpe Ratio

0.016Alpha

0.469Beta

5.28CAR

33.7Drawdown

48Loss Rate

10Parameters

1Security Types

0.0258Sortino Ratio

0Tradeable Dates

3094Trades

0.162Treynor Ratio

52Win Rate

8Parameters

1Security Types

0Tradeable Dates

8Parameters

1Security Types

0Tradeable Dates

35.313Net Profit

0.29Sharpe Ratio

0.024Alpha

0.13Beta

3.204CAR

33.4Drawdown

50Loss Rate

12Parameters

1Security Types

0.0317Sortino Ratio

0Tradeable Dates

1568Trades

0.318Treynor Ratio

50Win Rate

2.233Net Profit

5.218Sharpe Ratio

0.41Alpha

0.981Beta

123.911CAR

2.4Drawdown

0Loss Rate

13Parameters

2Security Types

0Sortino Ratio

8Tradeable Dates

13Trades

0.573Treynor Ratio

100Win Rate

7Parameters

1Security Types

0Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

3Parameters

2Security Types

0Sortino Ratio

11Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

-21.788Net Profit

-0.964Sharpe Ratio

-0.075Alpha

-0.005Beta

-9.074CAR

26Drawdown

55Loss Rate

2Parameters

1Security Types

0.0859Sortino Ratio

649Tradeable Dates

5628Trades

16.613Treynor Ratio

45Win Rate

-3.385Net Profit

-0.387Sharpe Ratio

0.012Alpha

-0.413Beta

-3.36CAR

5.7Drawdown

75Loss Rate

9Parameters

1Security Types

-1.0687Sortino Ratio

310Tradeable Dates

8Trades

0.062Treynor Ratio

25Win Rate

-7.108Net Profit

-0.032Sharpe Ratio

-0.003Alpha

0.019Beta

-0.357CAR

37.1Drawdown

51Loss Rate

10Parameters

1Security Types

-0.0083Sortino Ratio

5180Tradeable Dates

1564Trades

-0.098Treynor Ratio

49Win Rate

12.436Net Profit

0.724Sharpe Ratio

0.038Alpha

-0.962Beta

2.153CAR

3.6Drawdown

42Loss Rate

13Parameters

1Security Types

0.1022Sortino Ratio

1383Tradeable Dates

494Trades

-0.021Treynor Ratio

58Win Rate

7Parameters

1Security Types

0Sortino Ratio

3896Tradeable Dates

33.076Net Profit

4.01Sharpe Ratio

4.231Alpha

138.851Beta

104555.077CAR

27.6Drawdown

0Loss Rate

4Parameters

2Security Types

0Sortino Ratio

0Tradeable Dates

3Trades

0.045Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

2Security Types

0Sortino Ratio

165Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

3.342Net Profit

2.663Sharpe Ratio

0.144Alpha

8.896Beta

39.611CAR

2.2Drawdown

42Loss Rate

7Parameters

1Security Types

0.1629Sortino Ratio

0Tradeable Dates

219Trades

0.034Treynor Ratio

58Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

3Parameters

1Security Types

0Sortino Ratio

8Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

0Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

33.076Net Profit

4.01Sharpe Ratio

4.231Alpha

138.851Beta

104555.077CAR

27.6Drawdown

0Loss Rate

5Parameters

2Security Types

0Sortino Ratio

0Tradeable Dates

3Trades

0.045Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

0Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

1Parameters

1Security Types

0Sortino Ratio

64Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

25.203Net Profit

1.407Sharpe Ratio

-0.001Alpha

24.264Beta

55.825CAR

10.2Drawdown

44Loss Rate

3Parameters

2Security Types

0.0654Sortino Ratio

185Tradeable Dates

1144Trades

0.014Treynor Ratio

56Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

1Parameters

1Security Types

0Sortino Ratio

128Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

Daniel submitted the research Fama French Five Factors

The Fama French five-factor model is a widely used financial model that quantifies risk and estimates the expected return on equity. It builds upon the dividend discount model and incorporates five factors: market return, size, value, profitability, and investment pattern. This model is used to develop a stock-picking strategy that focuses on these factors. The strategy involves running a Coarse Selection to filter out equities with no fundamental data or low prices, and then selecting those with the highest dollar volume. The portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. The strategy can be improved by adjusting the fundamental factors, factor weights, and rebalancing frequency.

Daniel submitted the research Seasonality Effect Based On Same Calendar Month Returns

This tutorial discusses the implementation of a seasonality strategy based on historical same-calendar-month returns. The strategy is derived from a research paper on return seasonalities. Seasonality effects in algorithmic trading have been well-documented across various countries, stock returns, and portfolios. The strategy involves selecting the top 100 liquid securities with a price greater than $5 as the universe. For each security, the monthly return for the same-calendar month of the previous year is calculated. Long positions are taken on securities with high monthly returns, while short positions are taken on securities with low monthly returns. The algorithm is rebalanced and the strategy is repeated at the end of each month. The implementation achieves a Sharpe ratio of 0.128 relative to the S&P 500 over the past 10 years. Possible improvements include using multiple years of same-calendar-month returns, incorporating time effects in the returns, and using different criteria for initial universe selection.

Daniel submitted the research Risk Premia In Forex Markets

This tutorial discusses a risk premia strategy in Forex markets based on a skewness indicator. The strategy is derived from a paper that explores the relationship between risk premia and skewness. The tutorial outlines the method for selecting the Forex universe and provides backtest results. The results show a low annual return and suggest potential improvements such as diversifying the Forex universe, adjusting the thresholds for positions, and increasing the length of historical data. The community is encouraged to further develop and test this strategy with different symbols, thresholds, and historical data lengths. The reference to the paper is provided for further reading.

Daniel submitted the research Price And Earnings Momentum

This tutorial discusses a strategy based on the price and earnings momentum effect of stocks, derived from the paper "Momentum" by N. Jegadeesh and S. Titman. Price/return momentum refers to the tendency for stocks that perform well over a three to twelve month period to continue performing well, while stocks that perform poorly tend to continue performing poorly. Earnings momentum refers to the tendency for stocks with high earnings per share (EPS) to outperform stocks with low EPS. The tutorial outlines a quarterly-rebalanced stock strategy based on these momentum factors. The method involves selecting a coarse universe of assets based on volume, price, and fundamental data, and later applying a fine universe filter. The hope is that the community can further develop strategies based on these techniques.

Daniel started the discussion Forex Monday CCI Trend Strategy

Hi all, I would love to share an algorithm with you guys here! This algorithm, Forex Monday CCI...

Daniel started the discussion Seasonality Effect based on Same-Calendar Month Returns

Introduction

Daniel left a comment in the discussion R Connectivity and Data Artifacts, Particularly if using the online servers?

Hi Brad,Thank you for your feedback and we will take it into consideration. Please check out the...

Daniel left a comment in the discussion Prohibit entry orders after a certain time

Hi Gmamuze and Jason,Thank you for your posts. As Jason indicated, using Time in the OnData()...

Daniel left a comment in the discussion OnEndOfDay hit twice for "same" symbol

Hi Paul,Most of the time you will not need to work with these encoded strings and only need to work...

Daniel left a comment in the discussion Request for help

Hi Subarno,Thank you for posting your code. I think the CoarseSelectionFunction() part is not...

Daniel left a comment in the discussion What level of C# learning is sufficient to get started with basic Algo programming

Hi Ian,First, I would recommend you learn all the Boot Camps using C# since they are the most...

Daniel left a comment in the discussion Need help importing custom data

Hi Frost,Thank you for your post and your effort to study the NIFTY example. We should modify the...

5 years ago