Daniel Chen
Quant
Date Joined
24th March, 2019
Bootcamp progress
45% Complete
Achievements
Algorithm Sharing
Share an algorithm with the community by making your algorithm public.
2019-06-20 00:11:13
Backtester
Run at least 100 backtests on QuantConnect's cloud.
2019-06-26 21:41:47
Live Trading
Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2019-07-09 18:44:04
Parallel Live Trading
Run multiple live algorithms simultaneously.
2019-07-09 18:51:59
C# Programming
Choose C# as your preferred programming language.
2019-07-10 17:34:52
Python Programming
Choose Python as your preferred programming language.
2019-03-24 16:39:46
Popular
Receive at least 10 upvotes from different users in the community.
2019-08-21 10:45:54
Boot Camp Private
Complete a beginner bootcamp in the algorithm lab.
2019-06-19 00:56:17
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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 (64)
View More9Parameters
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
Community
View MoreDaniel submitted the research Fama French Five Factors
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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...
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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