Xin Wei
Quant
Date Joined
19th June, 2019
Bootcamp progress
11% Complete
Achievements
Algorithm Sharing
Share an algorithm with the community by making your algorithm public.
2019-06-20 15:59:21
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2019-07-12 16:18:12
Backtester
Run at least 100 backtests on QuantConnect's cloud.
2019-08-07 16:06:35
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Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2019-08-08 17:33:22
Python Programming
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2019-06-19 16:14:04
Popular
Receive at least 10 upvotes from different users in the community.
2019-07-25 17:00:31
Boot Camp Private
Complete a beginner bootcamp in the algorithm lab.
2019-08-15 01:01:49
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2019-07-10 22:55:33
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Public Backtests (33)
View More16.782Net 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
Community
View MoreXin submitted the research Standardized Unexpected Earnings
Abstract
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
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
Abstract
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
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,
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Xin left a comment in the discussion Universe Selection with warmup and SMA
Hi Armin,
5 years ago