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Quantitative Developer Intern at QuantConnect

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16.782Net Profit

0.177Sharpe Ratio

0.013Alpha

0.057Beta

1.618CAR

26.6Drawdown

53Loss Rate

72Parameters

1Security Types

0.0151Sortino Ratio

0Tradeable Dates

506Trades

0.333Treynor Ratio

47Win Rate

5Parameters

1Security Types

0Tradeable Dates

10Parameters

2Security Types

27Tradeable Dates

161.592Net Profit

0.969Sharpe Ratio

0.255Alpha

0.576Beta

31.607CAR

35.3Drawdown

52Loss Rate

5Parameters

1Security Types

0.7095Sortino Ratio

0Tradeable Dates

64Trades

0.574Treynor Ratio

48Win Rate

11Parameters

1Security Types

0Tradeable Dates

Xin submitted the research Standardized Unexpected Earnings

This tutorial discusses the implementation of a strategy that focuses on standardized unexpected earnings (SUE) of stocks. The strategy selects the top 5% of stocks based on their standardized unexpected earnings and trades them. The implementation narrows down the universe of stocks to 1000 liquid assets and considers factors such as daily trading volume, price, and availability of fundamental data. The unexpected earnings are calculated at the beginning of each month, standardized, and the top 5% stocks are selected for a long position. The portfolio is rebalanced monthly. The backtesting results show a Sharpe ratio of 0.602 relative to SPY Sharpe of 0.43 during the period from December 1, 2009, to September 1, 2019. The strategy is based on the theory that stock returns following earnings announcements exhibit anomalous behavior, suggesting market inefficiency. The method uses SUE, which is calculated as the difference between reported earnings and expected earnings, standardized by the standard deviation.

Xin submitted the research Expected Idiosyncratic Skewness

Abstract: This tutorial discusses the implementation of a trading strategy that focuses on stocks with low expected idiosyncratic skewness. The strategy is based on a paper by Boyer, Mitton, and Vorkink (2009) and involves narrowing down the initial universe of stocks to liquid assets based on trading volume, price, and availability of fundamental data. The expected idiosyncratic skewness is calculated at the end of each month, and the universe is sorted based on this measure. The strategy involves longing the bottom 5% of stocks, holding them for the next month, and rebalancing the portfolio monthly. The strategy has shown a Sharpe ratio of 0.947 relative to the S&P 500 Sharpe ratio of 0.87 during the period of July 1, 2009, to July 30, 2019.

Xin left a comment in the discussion Importing RSS Feed as Custom Data

Hi Chadwick,

Xin left a comment in the discussion OnSecuritiesChanged Questions

Hi Jason,

16.782Net Profit

0.177Sharpe Ratio

0.013Alpha

0.057Beta

1.618CAR

26.6Drawdown

53Loss Rate

72Parameters

1Security Types

0.0151Sortino Ratio

0Tradeable Dates

506Trades

0.333Treynor Ratio

47Win Rate

5Parameters

1Security Types

0Tradeable Dates

10Parameters

2Security Types

27Tradeable Dates

161.592Net Profit

0.969Sharpe Ratio

0.255Alpha

0.576Beta

31.607CAR

35.3Drawdown

52Loss Rate

5Parameters

1Security Types

0.7095Sortino Ratio

0Tradeable Dates

64Trades

0.574Treynor Ratio

48Win Rate

11Parameters

1Security Types

0Tradeable Dates

8Parameters

1Security Types

0Tradeable Dates

8Parameters

1Security Types

0Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

3Parameters

1Security Types

0Sortino Ratio

2Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

8Parameters

1Security Types

0Tradeable Dates

1421.211Net Profit

1.216Sharpe Ratio

0.118Alpha

1.208Beta

31.257CAR

36.6Drawdown

39Loss Rate

68Parameters

1Security Types

0.1923Sortino Ratio

2517Tradeable Dates

2839Trades

0.238Treynor Ratio

61Win Rate

852.706Net Profit

1.059Sharpe Ratio

0.074Alpha

1.192Beta

25.261CAR

33.5Drawdown

37Loss Rate

27Parameters

1Security Types

0.144Sortino Ratio

2517Tradeable Dates

2820Trades

0.203Treynor Ratio

63Win Rate

8.995Net Profit

1.126Sharpe Ratio

2.125Alpha

-3.09Beta

66.041CAR

19Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

62Tradeable Dates

1Trades

-0.215Treynor Ratio

0Win Rate

22.843Net Profit

1.794Sharpe Ratio

0.268Alpha

1.374Beta

51.089CAR

17.9Drawdown

0Loss Rate

4Parameters

1Security Types

0Sortino Ratio

126Tradeable Dates

1Trades

0.319Treynor Ratio

0Win Rate

141.947Net Profit

0.852Sharpe Ratio

0.113Alpha

-0.007Beta

13.442CAR

18.3Drawdown

54Loss Rate

21Parameters

1Security Types

1.4716Sortino Ratio

1763Tradeable Dates

219Trades

-16.168Treynor Ratio

46Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

2Parameters

1Security Types

0Sortino Ratio

125Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0.302Net Profit

11.225Sharpe Ratio

0Alpha

55.142Beta

93.532CAR

0.2Drawdown

0Loss Rate

3Parameters

1Security Types

0Sortino Ratio

2Tradeable Dates

1Trades

0.007Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

3Parameters

1Security Types

0Sortino Ratio

3Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

14.255Net Profit

0.172Sharpe Ratio

0.034Alpha

-1.089Beta

1.412CAR

21.5Drawdown

50Loss Rate

77Parameters

1Security Types

0.0251Sortino Ratio

0Tradeable Dates

562Trades

-0.014Treynor Ratio

50Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

501Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

44Parameters

1Security Types

0Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

34Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

1Parameters

1Security Types

0Sortino Ratio

2Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

-14.149Net Profit

-1.431Sharpe Ratio

0.492Alpha

-40.323Beta

-28.553CAR

21.8Drawdown

77Loss Rate

4Parameters

1Security Types

0.006Sortino Ratio

114Tradeable Dates

6934Trades

0.008Treynor Ratio

23Win Rate

41.07Net Profit

0.465Sharpe Ratio

0.033Alpha

0.48Beta

3.959CAR

12.2Drawdown

49Loss Rate

91Parameters

1Security Types

0.0593Sortino Ratio

2231Tradeable Dates

508Trades

0.087Treynor Ratio

51Win Rate

0.164Net Profit

6.588Sharpe Ratio

0.065Alpha

-0.124Beta

6.271CAR

0.1Drawdown

0Loss Rate

8Parameters

2Security Types

0Sortino Ratio

6Tradeable Dates

3Trades

-0.371Treynor Ratio

0Win Rate

10Parameters

1Security Types

6Tradeable Dates

0Parameters

1Security Types

91Tradeable Dates

0Net Profit

0Sharpe Ratio

0Alpha

0Beta

0CAR

0Drawdown

0Loss Rate

0Parameters

1Security Types

0Sortino Ratio

12Tradeable Dates

0Trades

0Treynor Ratio

0Win Rate

-31.924Net Profit

-16.807Sharpe Ratio

-6.284Alpha

-440.5Beta

-100CAR

32.2Drawdown

75Loss Rate

11Parameters

2Security Types

-0.9585Sortino Ratio

6Tradeable Dates

26Trades

0.026Treynor Ratio

25Win Rate

2Parameters

1Security Types

2Tradeable Dates

Xin submitted the research Standardized Unexpected Earnings

This tutorial discusses the implementation of a strategy that focuses on standardized unexpected earnings (SUE) of stocks. The strategy selects the top 5% of stocks based on their standardized unexpected earnings and trades them. The implementation narrows down the universe of stocks to 1000 liquid assets and considers factors such as daily trading volume, price, and availability of fundamental data. The unexpected earnings are calculated at the beginning of each month, standardized, and the top 5% stocks are selected for a long position. The portfolio is rebalanced monthly. The backtesting results show a Sharpe ratio of 0.602 relative to SPY Sharpe of 0.43 during the period from December 1, 2009, to September 1, 2019. The strategy is based on the theory that stock returns following earnings announcements exhibit anomalous behavior, suggesting market inefficiency. The method uses SUE, which is calculated as the difference between reported earnings and expected earnings, standardized by the standard deviation.

Xin submitted the research Expected Idiosyncratic Skewness

Abstract: This tutorial discusses the implementation of a trading strategy that focuses on stocks with low expected idiosyncratic skewness. The strategy is based on a paper by Boyer, Mitton, and Vorkink (2009) and involves narrowing down the initial universe of stocks to liquid assets based on trading volume, price, and availability of fundamental data. The expected idiosyncratic skewness is calculated at the end of each month, and the universe is sorted based on this measure. The strategy involves longing the bottom 5% of stocks, holding them for the next month, and rebalancing the portfolio monthly. The strategy has shown a Sharpe ratio of 0.947 relative to the S&P 500 Sharpe ratio of 0.87 during the period of July 1, 2009, to July 30, 2019.

Xin left a comment in the discussion Universe Selection with warmup and SMA

Hi Armin,

Xin left a comment in the discussion Importing RSS Feed as Custom Data

Hi Chadwick,

Xin left a comment in the discussion OnSecuritiesChanged Questions

Hi Jason,

Xin left a comment in the discussion Log Data

Hi Nocholas,

Xin left a comment in the discussion Documentation discussion research/historical-data

Hi Omegab,

Xin left a comment in the discussion Is it possible to get other market data such as minute data from Bovespa exchange?

Hi Bruno,

Xin left a comment in the discussion Universe Selection with warmup and SMA

Hi Armin,

5 years ago