Shile Wen
Quant
Date Joined
14th October, 2019
Bootcamp progress
85% Complete
Achievements
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Share an algorithm with the community by making your algorithm public.
2020-05-25 02:00:26
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2020-07-20 20:48:02
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Run at least 100 backtests on QuantConnect's cloud.
2020-06-18 05:00:48
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Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2020-05-18 17:30:01
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Run multiple live algorithms simultaneously.
2020-07-07 16:47:00
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Clone at least 100 algorithms from the community.
2021-12-11 21:57:12
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2020-07-14 17:00:50
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Choose Python as your preferred programming language.
2019-10-14 20:34:39
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Receive at least 10 upvotes from different users in the community.
2020-07-02 14:44:11
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2020-05-15 00:24:28
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2020-05-22 23:28:46
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Public Backtests (811)
View More-13.978Net Profit
2.959PSR
-1.087Sharpe Ratio
-0.195Alpha
0.64Beta
-26.058CAR
24.1Drawdown
-0.3Loss Rate
5Parameters
0Security Types
-1.663Sortino Ratio
0Tradeable Dates
96Trades
-0.278Treynor Ratio
0.05Win Rate
0.468Net Profit
38.527PSR
0.254Sharpe Ratio
-0.05Alpha
0.357Beta
4.404CAR
8.6Drawdown
100Loss Rate
28Parameters
2Security Types
0Sortino Ratio
238Tradeable Dates
7Trades
0.2Treynor Ratio
0Win Rate
-5.982Net Profit
4.797PSR
-3.334Sharpe Ratio
-0.453Alpha
-0.441Beta
-48.81CAR
6.6Drawdown
0Loss Rate
19Parameters
0Security Types
-4.632Sortino Ratio
24Tradeable Dates
16Trades
1.07Treynor Ratio
0Win Rate
-19.441Net Profit
21.131PSR
-0.794Sharpe Ratio
2.403Alpha
-6.316Beta
-84.37CAR
27.4Drawdown
0Loss Rate
20Parameters
0Security Types
-1.424Sortino Ratio
273Tradeable Dates
10001Trades
0.121Treynor Ratio
0.02Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
25Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
2Tradeable Dates
10Trades
0Treynor Ratio
0Win Rate
Community
View MoreShile started the discussion My QuantConnect workflow (as a previous QuantConnect support staff)
Hi Everyone,
Shile submitted the research Forecasting Stock Prices Using A Temporal CNN Model
Abstract
This tutorial focuses on using a Temporal Convolutional Neural Network (CNN) model to forecast stock prices. The goal is to develop a model that can predict the movement of future prices based on historical data. The input to the model is OHLC+Volume data for the past 15 time steps, and the output is the direction of the movement of the average close for the next 5 time steps. The model is built using Keras, a Python Deep Learning API. The code required to build the Neural Network Architecture and prepare the data is explained in detail. The tutorial also mentions that the algorithm is non-deterministic, so different results may be seen in repeated backtests.
Shile submitted the research Leveraged ETFs With Systematic Risk Management
Abstract
This tutorial explores the use of leverage and systematic risk management to outperform the S&P500 Index. The authors propose using leveraged S&P500 indices with systematic risk management, utilizing moving averages to assess market volatility and manage risk. The method involves holding or liquidating positions based on the 200-day Simple Moving Average (SMA) of the SSO ETF, a 2x leveraged S&P500 index ETF. If the current price of SSO is above the 200-day SMA, the position is held, and if it dips below, the position is liquidated and rotated into short-term treasuries using the SHY ETF. The results show a higher Sharpe Ratio for this strategy compared to buying and holding the SPY ETF.
Shile submitted the research Optimal Pairs Trading
Abstract
Abstract: This tutorial explores the application of the Ornstein-Uhlenbeck model to pairs trading and the derivation of optimal entry and liquidation levels. Pairs trading involves holding one stock while shorting another stock in order to profit from the convergence of their spread. The tutorial discusses the use of a Kalman Filter for execution and proposes modeling the portfolio values as an Ornstein-Uhlenbeck process. The tutorial outlines the method for computing the Ornstein-Uhlenbeck coefficients and selecting the optimal beta value. It also explains how to derive the optimal entry and liquidation levels based on the coefficients. The implementation details can be found in the provided code.
-13.978Net Profit
2.959PSR
-1.087Sharpe Ratio
-0.195Alpha
0.64Beta
-26.058CAR
24.1Drawdown
-0.3Loss Rate
5Parameters
0Security Types
-1.663Sortino Ratio
0Tradeable Dates
96Trades
-0.278Treynor Ratio
0.05Win Rate
0.468Net Profit
38.527PSR
0.254Sharpe Ratio
-0.05Alpha
0.357Beta
4.404CAR
8.6Drawdown
100Loss Rate
28Parameters
2Security Types
0Sortino Ratio
238Tradeable Dates
7Trades
0.2Treynor Ratio
0Win Rate
-5.982Net Profit
4.797PSR
-3.334Sharpe Ratio
-0.453Alpha
-0.441Beta
-48.81CAR
6.6Drawdown
0Loss Rate
19Parameters
0Security Types
-4.632Sortino Ratio
24Tradeable Dates
16Trades
1.07Treynor Ratio
0Win Rate
-19.441Net Profit
21.131PSR
-0.794Sharpe Ratio
2.403Alpha
-6.316Beta
-84.37CAR
27.4Drawdown
0Loss Rate
20Parameters
0Security Types
-1.424Sortino Ratio
273Tradeable Dates
10001Trades
0.121Treynor Ratio
0.02Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
25Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
2Tradeable Dates
10Trades
0Treynor Ratio
0Win Rate
7.679Net Profit
41.931PSR
0.867Sharpe Ratio
-0.371Alpha
6.394Beta
22.841CAR
46.6Drawdown
-4.22Loss Rate
42Parameters
0Security Types
0.059Sortino Ratio
92Tradeable Dates
48Trades
0.109Treynor Ratio
2.61Win Rate
-5.627Net Profit
10.613PSR
-1.359Sharpe Ratio
-0.285Alpha
-0.169Beta
-28.892CAR
11.6Drawdown
-1.3Loss Rate
39Parameters
0Security Types
-1.161Sortino Ratio
41Tradeable Dates
33Trades
1.683Treynor Ratio
0.43Win Rate
-5.627Net Profit
10.613PSR
-1.359Sharpe Ratio
-0.285Alpha
-0.169Beta
-28.892CAR
11.6Drawdown
-1.3Loss Rate
39Parameters
0Security Types
-1.161Sortino Ratio
41Tradeable Dates
33Trades
1.683Treynor Ratio
0.43Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
42Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
7Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
42Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
7Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
42Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
7Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
42Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
7Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-80.423Net Profit
0.015PSR
-0.455Sharpe Ratio
-1.002Alpha
1.46Beta
-100CAR
81.6Drawdown
98Loss Rate
39Parameters
2Security Types
-7.117Sortino Ratio
920Tradeable Dates
156Trades
-0.685Treynor Ratio
2Win Rate
-13.301Net Profit
0.195PSR
-1.312Sharpe Ratio
-0.111Alpha
0.067Beta
-13.101CAR
16.3Drawdown
-0.38Loss Rate
17Parameters
0Security Types
-1.703Sortino Ratio
316Tradeable Dates
739Trades
-1.489Treynor Ratio
0.48Win Rate
25.78Net Profit
67.385PSR
2.19Sharpe Ratio
0.185Alpha
2.369Beta
72.812CAR
17.4Drawdown
0Loss Rate
13Parameters
0Security Types
2.53Sortino Ratio
130Tradeable Dates
5Trades
0.29Treynor Ratio
11.91Win Rate
-29.167Net Profit
12.016PSR
0.24Sharpe Ratio
-0.029Alpha
1.769Beta
-15.838CAR
89Drawdown
-17.89Loss Rate
11Parameters
0Security Types
-0.073Sortino Ratio
620Tradeable Dates
10Trades
0.088Treynor Ratio
17.53Win Rate
396.383Net Profit
90.566PSR
6.932Sharpe Ratio
5.998Alpha
2.288Beta
395.795CAR
34.1Drawdown
-2.43Loss Rate
38Parameters
0Security Types
5.651Sortino Ratio
253Tradeable Dates
209Trades
2.776Treynor Ratio
3.61Win Rate
12.008Net Profit
37.144PSR
0.826Sharpe Ratio
0.024Alpha
0.083Beta
3.718CAR
5.4Drawdown
-0.66Loss Rate
34Parameters
0Security Types
0.723Sortino Ratio
780Tradeable Dates
200Trades
0.465Treynor Ratio
1.02Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
30Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
277Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0PSR
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
20Parameters
0Security Types
7.9228162514264E+28Sortino Ratio
10Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
47.893Net Profit
59.821PSR
1.226Sharpe Ratio
0.094Alpha
0.268Beta
13.427CAR
9.6Drawdown
-2.4Loss Rate
20Parameters
0Security Types
1.109Sortino Ratio
780Tradeable Dates
34Trades
0.531Treynor Ratio
5.89Win Rate
55.499Net Profit
10.618PSR
0.51Sharpe Ratio
0.101Alpha
-0.05Beta
9.223CAR
31.4Drawdown
-0.41Loss Rate
16Parameters
0Security Types
0.517Sortino Ratio
1259Tradeable Dates
1712Trades
-1.864Treynor Ratio
0.4Win Rate
91.721Net Profit
7.509PSR
0.516Sharpe Ratio
0.118Alpha
-0.05Beta
10.528CAR
39.2Drawdown
-0.41Loss Rate
16Parameters
1Security Types
0.513Sortino Ratio
1637Tradeable Dates
2156Trades
-2.207Treynor Ratio
0.54Win Rate
68.553Net Profit
4.56PSR
0.419Sharpe Ratio
0.101Alpha
-0.023Beta
8.36CAR
42.2Drawdown
-0.49Loss Rate
16Parameters
1Security Types
0.334Sortino Ratio
1637Tradeable Dates
1966Trades
-4.22Treynor Ratio
0.52Win Rate
69.069Net Profit
4.551PSR
0.418Sharpe Ratio
0.103Alpha
-0.025Beta
8.411CAR
42.6Drawdown
-0.5Loss Rate
15Parameters
1Security Types
0.33Sortino Ratio
1637Tradeable Dates
1932Trades
-3.929Treynor Ratio
0.53Win Rate
179.391Net Profit
76.855PSR
2.156Sharpe Ratio
1.439Alpha
-0.926Beta
91.552CAR
21.1Drawdown
27Loss Rate
32Parameters
2Security Types
0.8525Sortino Ratio
398Tradeable Dates
56Trades
-1.279Treynor Ratio
73Win Rate
-3.333Net Profit
7.576PSR
-3.387Sharpe Ratio
-0.157Alpha
0.388Beta
-32.066CAR
5.8Drawdown
0Loss Rate
34Parameters
0Security Types
-4.897Sortino Ratio
20Tradeable Dates
3Trades
-0.853Treynor Ratio
0Win Rate
-34.182Net Profit
2.565PSR
-0.618Sharpe Ratio
0.006Alpha
0.916Beta
-26.913CAR
49.4Drawdown
-0.24Loss Rate
27Parameters
0Security Types
-1.144Sortino Ratio
336Tradeable Dates
21Trades
-0.24Treynor Ratio
0Win Rate
17.436Net Profit
74.799PSR
2.237Sharpe Ratio
0.074Alpha
0.72Beta
33.63CAR
7.9Drawdown
40Loss Rate
28Parameters
2Security Types
-0.6191Sortino Ratio
645Tradeable Dates
11Trades
0.509Treynor Ratio
60Win Rate
44.871Net Profit
54.325PSR
1.248Sharpe Ratio
0.057Alpha
0.867Beta
26.792CAR
27.7Drawdown
0Loss Rate
9Parameters
1Security Types
0Sortino Ratio
393Tradeable Dates
13Trades
0.354Treynor Ratio
100Win Rate
Shile started the discussion My QuantConnect workflow (as a previous QuantConnect support staff)
Hi Everyone,
Shile left a comment in the discussion Strategy Library Addition: Forecasting Stock Prices using a Temporal CNN Model
Hi Carpediem911,
Shile submitted the research Forecasting Stock Prices Using A Temporal CNN Model
Abstract
This tutorial focuses on using a Temporal Convolutional Neural Network (CNN) model to forecast stock prices. The goal is to develop a model that can predict the movement of future prices based on historical data. The input to the model is OHLC+Volume data for the past 15 time steps, and the output is the direction of the movement of the average close for the next 5 time steps. The model is built using Keras, a Python Deep Learning API. The code required to build the Neural Network Architecture and prepare the data is explained in detail. The tutorial also mentions that the algorithm is non-deterministic, so different results may be seen in repeated backtests.
Shile submitted the research Leveraged ETFs With Systematic Risk Management
Abstract
This tutorial explores the use of leverage and systematic risk management to outperform the S&P500 Index. The authors propose using leveraged S&P500 indices with systematic risk management, utilizing moving averages to assess market volatility and manage risk. The method involves holding or liquidating positions based on the 200-day Simple Moving Average (SMA) of the SSO ETF, a 2x leveraged S&P500 index ETF. If the current price of SSO is above the 200-day SMA, the position is held, and if it dips below, the position is liquidated and rotated into short-term treasuries using the SHY ETF. The results show a higher Sharpe Ratio for this strategy compared to buying and holding the SPY ETF.
Shile submitted the research Optimal Pairs Trading
Abstract
Abstract: This tutorial explores the application of the Ornstein-Uhlenbeck model to pairs trading and the derivation of optimal entry and liquidation levels. Pairs trading involves holding one stock while shorting another stock in order to profit from the convergence of their spread. The tutorial discusses the use of a Kalman Filter for execution and proposes modeling the portfolio values as an Ornstein-Uhlenbeck process. The tutorial outlines the method for computing the Ornstein-Uhlenbeck coefficients and selecting the optimal beta value. It also explains how to derive the optimal entry and liquidation levels based on the coefficients. The implementation details can be found in the provided code.
Shile submitted the research G Score Investing
Abstract
This tutorial discusses G-Score Investing, a method of trading that involves analyzing a company's fundamentals to make investment decisions. The tutorial explains the concept of Factor Investing and how G-Score investing evaluates companies based on seven factors, with a focus on Book-to-Market ratio. The method involves sorting companies by their Book-to-Market ratio and selecting the bottom quartile. The tutorial also mentions that fundamental data used in the algorithms is sourced from MorningStar. The results of the algorithm are compared to the performance of the QQQ ETF as a benchmark. The tutorial concludes by referencing a paper by Mohanram on separating winners from losers among low Book-to-Market stocks using financial statement analysis.
Shile submitted the research SVM Wavelet Forecasting
Abstract
In this tutorial, the authors explore the application of an SVM Wavelet model for forecasting EURJPY prices. They combine a Support Vector Machine (SVM) with Wavelets to handle non-linear data and decompose the time-series into multiple components. The SVM is then applied to forecast one time-step ahead for each component, and the components are recombined to obtain the aggregate forecast. The algorithm achieved a Sharpe Ratio of 0.553, outperforming buying and holding SPY. Suggestions for improvement include testing different Wavelet types, trying out other time resolutions, and using alternative Decomposition methods. The authors encourage users to share any interesting results or modifications in the Community Forum.
Shile started the discussion Strategy Library Addition: Leveraged ETFs with Systematic Risk Management
Hi Everyone,
Shile started the discussion Strategy Library Addition: Forecasting Stock Prices using a Temporal CNN Model
Hi Everyone,
Shile left a comment in the discussion Is there a way to pass indicators from Universe Selection Model to Alpha Model?
Hi Fishstoryyy,
Shile left a comment in the discussion Rolling Window not working Lean Locally
Hi Ishant,
Shile left a comment in the discussion Need help with quarterly or biweekly rebalancing in Algorithm Framework
Hi Rakesh,
Shile left a comment in the discussion Rolling Window of Consolidated bars
Hi Luke,
Shile left a comment in the discussion Implementing Black-Litterman Port Construction with EMA, SMA and MACD Alphas
Hi Miguel,
Shile started the discussion My Pairs Trading Algorithm has a near perfect linear downward trend
I found this pretty interesting. If someone finds a way to take advantage of this, please let me...
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Shile left a comment in the discussion Strategy Library Addition: Forecasting Stock Prices using a Temporal CNN Model
Hi Carpediem911,
3 years ago