Jing Wu
Quant
Date Joined
25th April, 2017
Bootcamp progress
15% Complete
Achievements
Algorithm Sharing
Share an algorithm with the community by making your algorithm public.
2018-01-29 14:25:08
Notebook Sharing
Share a notebook with others.
2018-04-06 20:10:30
Chatter Box
Leave at least 100 comments on community posts.
2018-01-29 22:03:58
Backtester
Run at least 100 backtests on QuantConnect's cloud.
2018-01-26 14:16:19
Live Trading
Deploy a live trading algorithm using one of QuantConnect's supported brokerages.
2018-02-01 16:58:34
Parallel Live Trading
Run multiple live algorithms simultaneously.
2018-02-02 14:14:56
C# Programming
Choose C# as your preferred programming language.
2018-03-26 16:52:55
Python Programming
Choose Python as your preferred programming language.
2018-04-04 13:33:50
Popular
Receive at least 10 upvotes from different users in the community.
2018-01-24 17:53:10
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Public Backtests (473)
View More-0.841Net Profit
-0.291Sharpe Ratio
-0.033Alpha
1.245Beta
-0.822CAR
4.6Drawdown
70Loss Rate
1Security Types
0.034045337798946Sortino Ratio
0Tradeable Dates
2110Trades
-0.006Treynor Ratio
30Win Rate
-0.895Net Profit
-6.238Sharpe Ratio
-0.263Alpha
9.461Beta
-10.761CAR
1.1Drawdown
100Loss Rate
2Security Types
0Sortino Ratio
20Tradeable Dates
2Trades
-0.01Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
2Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
42.806Net Profit
8.739Sharpe Ratio
-18.211Alpha
2577.436Beta
19792170106220CAR
4.6Drawdown
48Loss Rate
1Security Types
4.6467030010054Sortino Ratio
3Tradeable Dates
51Trades
0.01Treynor Ratio
52Win Rate
-5.501Net Profit
-0.296Sharpe Ratio
0.323Alpha
-18.634Beta
-6.031CAR
11.5Drawdown
100Loss Rate
1Security Types
0Sortino Ratio
228Tradeable Dates
3Trades
0.003Treynor Ratio
0Win Rate
Community
View MoreJing submitted the research Short Term Reversal Strategy In Stocks
Abstract
Abstract: This tutorial focuses on implementing a short-term reversal strategy in stocks based on the research conducted by De Groot, Huij, & Zhou (2012). The strategy involves selecting the worst performers and shorting the top performers in the universe of stocks on a weekly basis. However, in this implementation, we limit the universe to the most liquid large cap stocks to reduce trading costs. Our analysis reveals that this strategy generally underperforms the S&P 500 index, except during the 2020 market crash. The strategy code is divided into four main parts: Initialization, Universe Selection, OnData, and OnSecuritiesChanged. The initialization phase involves setting parameters and implementing a coarse universe selection method. The relative performance of the strategy and benchmark is evaluated over different time periods, including a 5-year backtest, the 2020 market crash, and the subsequent recovery.
Jing submitted the research Stock Selection Strategy Based On Fundamental Factors
Abstract
Abstract: This tutorial explores a stock selection strategy based on fundamental factors. The first step involves developing a factor selection model to determine if factors can differentiate potential winners and losers in the stock market. The selected factors are then used to implement a factor ranking stock selection algorithm for Turkish equities. The algorithm ranks stocks based on their factor values and calculates an equally weighted composite factor score for each stock. The stocks are then placed into quintile portfolios based on their factor scores, and the highest ranked 20 stocks are selected to construct portfolios at the beginning of each month. This process is repeated at the end of each month to construct new portfolios.
Jing submitted the research The Dynamic Breakout II Strategy
Abstract
Abstract: This tutorial discusses the Dynamic Breakout II strategy, which is based on the book "Building Winning Trading Systems". The strategy involves determining the look-back period based on volatility change rate and making trading decisions based on the highest high, lowest low, and Bollinger Bands indicator. The strategy is auto adaptive and can adjust its rules based on past performance. It is commonly used in Forex, Future, and Equity markets. The newer version of the strategy introduces the Bollinger Band and adjusts the look-back days using market volatility. The stop loss signal is also dynamically changed with the look-back period. Backtesting on EURUSD and GBPUSD over a 6-year period showed a drawdown of 20% and profitability in trending markets.
Jing submitted the research The Momentum Strategy Based On The Low Frequency Component Of Forex Market
Abstract
Abstract: This discussion focuses on a momentum trading strategy in the Forex market based on the low frequency component of the exchange rate. The strategy utilizes the Hodrick-Prescott Filter to generate the non-linear trend component, which is then measured using the MA(1, 2) rule. The performance of the strategy was tested on seven exchange rates, showing less robustness and sensitivity to model parameter changes. The Hodrick-Prescott Filter decomposes the time series into cyclical and trend components through an optimization problem. Trading signals were generated using MA rules on the low-frequency component, with smoother trends observed with larger values of lambda.
Jing submitted the research Combining Mean Reversion And Momentum In Forex Market
Abstract
In this tutorial, we explore a strategy that combines momentum and mean reversion in the foreign exchange (forex) market. The strategy is based on research by Alina F. Serban, who adapted the concept from research in the equity market by Ronald J. Balvers and Yangru Wu. Serban creates a momentum factor using returns from the past three months and a mean reversion factor based on the deviation from the mean price. Regression analysis is used to predict the returns for the next month. The strategy is applied to the forex market, specifically the EURUSD, GBPUSD, USDCAD, and USDJPY pairs, with monthly rebalancing. The model's significance level and coefficients are similar to those in Serban's paper, but the returns and Sharpe Ratios obtained are not as good as claimed. The algorithm achieves a stable annual return of 11%, a 0.8 Sharpe Ratio, and an 11% drawdown. The strategy is centered on the theory of uncovered interest parity (UIP), which states that exchange rate changes should incorporate interest rate differentials. By identifying deviations from UIP, abnormal returns can be generated. The strategy uses the deviation from UIP as an indicator. The tutorial provides an overview of the interest parity conditions and the model and parameter estimation process.
-0.841Net Profit
-0.291Sharpe Ratio
-0.033Alpha
1.245Beta
-0.822CAR
4.6Drawdown
70Loss Rate
1Security Types
0.034045337798946Sortino Ratio
0Tradeable Dates
2110Trades
-0.006Treynor Ratio
30Win Rate
-0.895Net Profit
-6.238Sharpe Ratio
-0.263Alpha
9.461Beta
-10.761CAR
1.1Drawdown
100Loss Rate
2Security Types
0Sortino Ratio
20Tradeable Dates
2Trades
-0.01Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
2Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
42.806Net Profit
8.739Sharpe Ratio
-18.211Alpha
2577.436Beta
19792170106220CAR
4.6Drawdown
48Loss Rate
1Security Types
4.6467030010054Sortino Ratio
3Tradeable Dates
51Trades
0.01Treynor Ratio
52Win Rate
-5.501Net Profit
-0.296Sharpe Ratio
0.323Alpha
-18.634Beta
-6.031CAR
11.5Drawdown
100Loss Rate
1Security Types
0Sortino Ratio
228Tradeable Dates
3Trades
0.003Treynor Ratio
0Win Rate
-0.018Net Profit
-9.165Sharpe Ratio
0Alpha
-1.643Beta
-3.232CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
2Tradeable Dates
2Trades
0.009Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
7Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
94.96Net Profit
0.539Sharpe Ratio
0.094Alpha
-1.178Beta
6.351CAR
26.9Drawdown
26Loss Rate
1Security Types
0.002324753740831Sortino Ratio
2730Tradeable Dates
7911Trades
-0.059Treynor Ratio
74Win Rate
4.741Net Profit
0.671Sharpe Ratio
0.001Alpha
0.208Beta
2.338CAR
3.2Drawdown
82Loss Rate
2Security Types
-0.070645065324453Sortino Ratio
505Tradeable Dates
32Trades
0.111Treynor Ratio
18Win Rate
5.018Net Profit
0.638Sharpe Ratio
0.014Alpha
0.179Beta
2.472CAR
3.8Drawdown
76Loss Rate
2Security Types
0.073663642775754Sortino Ratio
505Tradeable Dates
31Trades
0.137Treynor Ratio
24Win Rate
12.636Net Profit
1.483Sharpe Ratio
0.026Alpha
0.317Beta
6.116CAR
4.2Drawdown
100Loss Rate
1Security Types
-1.4694920148339Sortino Ratio
505Tradeable Dates
9Trades
0.189Treynor Ratio
0Win Rate
5.884Net Profit
0.6Sharpe Ratio
0.014Alpha
0.255Beta
2.893CAR
7.2Drawdown
100Loss Rate
1Security Types
0Sortino Ratio
505Tradeable Dates
3Trades
0.117Treynor Ratio
0Win Rate
5.316Net Profit
0.97Sharpe Ratio
0.011Alpha
0.138Beta
2.618CAR
2.5Drawdown
58Loss Rate
1Security Types
1.6255065548246Sortino Ratio
505Tradeable Dates
38Trades
0.189Treynor Ratio
42Win Rate
4.355Net Profit
0.611Sharpe Ratio
0.013Alpha
0.136Beta
2.149CAR
3.8Drawdown
58Loss Rate
1Security Types
0.33654830516771Sortino Ratio
505Tradeable Dates
38Trades
0.161Treynor Ratio
42Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
4Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
41Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
394.166Net Profit
0.707Sharpe Ratio
0.234Alpha
-1.689Beta
17.321CAR
43.4Drawdown
29Loss Rate
1Security Types
-0.039697071293318Sortino Ratio
2518Tradeable Dates
7472Trades
-0.119Treynor Ratio
71Win Rate
-17.95Net Profit
-4.601Sharpe Ratio
-2.538Alpha
50.234Beta
-90.992CAR
21.6Drawdown
39Loss Rate
1Security Types
-0.12098452772387Sortino Ratio
26Tradeable Dates
181Trades
-0.035Treynor Ratio
61Win Rate
20.724Net Profit
0.686Sharpe Ratio
0.067Alpha
0.425Beta
6.469CAR
13Drawdown
19Loss Rate
2Security Types
0.033140992305047Sortino Ratio
756Tradeable Dates
57Trades
0.154Treynor Ratio
81Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
9Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
9Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
20.904Net Profit
0.875Sharpe Ratio
0.213Alpha
-7.448Beta
10.896CAR
13.1Drawdown
36Loss Rate
1Security Types
0.078369262839573Sortino Ratio
463Tradeable Dates
3797Trades
-0.012Treynor Ratio
64Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
9Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
352.627Net Profit
1.01Sharpe Ratio
0.067Alpha
5.98Beta
18.623CAR
19.4Drawdown
4Loss Rate
1Security Types
4.7986775746449Sortino Ratio
2225Tradeable Dates
427Trades
0.031Treynor Ratio
96Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
18Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
2Security Types
0Sortino Ratio
1Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
5Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
-9.719Net Profit
-5.319Sharpe Ratio
-1.133Alpha
1.94Beta
-69.995CAR
9.7Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
21Tradeable Dates
1Trades
-0.565Treynor Ratio
0Win Rate
0.243Net Profit
2.174Sharpe Ratio
0.136Alpha
-4.514Beta
7.67CAR
0.3Drawdown
83Loss Rate
1Security Types
0.20044758119505Sortino Ratio
0Tradeable Dates
24Trades
-0.014Treynor Ratio
17Win Rate
0Net Profit
0Sharpe Ratio
0Alpha
0Beta
0CAR
0Drawdown
0Loss Rate
1Security Types
0Sortino Ratio
27Tradeable Dates
0Trades
0Treynor Ratio
0Win Rate
Jing submitted the research Short Term Reversal Strategy In Stocks
Abstract
Abstract: This tutorial focuses on implementing a short-term reversal strategy in stocks based on the research conducted by De Groot, Huij, & Zhou (2012). The strategy involves selecting the worst performers and shorting the top performers in the universe of stocks on a weekly basis. However, in this implementation, we limit the universe to the most liquid large cap stocks to reduce trading costs. Our analysis reveals that this strategy generally underperforms the S&P 500 index, except during the 2020 market crash. The strategy code is divided into four main parts: Initialization, Universe Selection, OnData, and OnSecuritiesChanged. The initialization phase involves setting parameters and implementing a coarse universe selection method. The relative performance of the strategy and benchmark is evaluated over different time periods, including a 5-year backtest, the 2020 market crash, and the subsequent recovery.
Jing submitted the research Stock Selection Strategy Based On Fundamental Factors
Abstract
Abstract: This tutorial explores a stock selection strategy based on fundamental factors. The first step involves developing a factor selection model to determine if factors can differentiate potential winners and losers in the stock market. The selected factors are then used to implement a factor ranking stock selection algorithm for Turkish equities. The algorithm ranks stocks based on their factor values and calculates an equally weighted composite factor score for each stock. The stocks are then placed into quintile portfolios based on their factor scores, and the highest ranked 20 stocks are selected to construct portfolios at the beginning of each month. This process is repeated at the end of each month to construct new portfolios.
Jing submitted the research The Dynamic Breakout II Strategy
Abstract
Abstract: This tutorial discusses the Dynamic Breakout II strategy, which is based on the book "Building Winning Trading Systems". The strategy involves determining the look-back period based on volatility change rate and making trading decisions based on the highest high, lowest low, and Bollinger Bands indicator. The strategy is auto adaptive and can adjust its rules based on past performance. It is commonly used in Forex, Future, and Equity markets. The newer version of the strategy introduces the Bollinger Band and adjusts the look-back days using market volatility. The stop loss signal is also dynamically changed with the look-back period. Backtesting on EURUSD and GBPUSD over a 6-year period showed a drawdown of 20% and profitability in trending markets.
Jing submitted the research The Momentum Strategy Based On The Low Frequency Component Of Forex Market
Abstract
Abstract: This discussion focuses on a momentum trading strategy in the Forex market based on the low frequency component of the exchange rate. The strategy utilizes the Hodrick-Prescott Filter to generate the non-linear trend component, which is then measured using the MA(1, 2) rule. The performance of the strategy was tested on seven exchange rates, showing less robustness and sensitivity to model parameter changes. The Hodrick-Prescott Filter decomposes the time series into cyclical and trend components through an optimization problem. Trading signals were generated using MA rules on the low-frequency component, with smoother trends observed with larger values of lambda.
Jing submitted the research Combining Mean Reversion And Momentum In Forex Market
Abstract
In this tutorial, we explore a strategy that combines momentum and mean reversion in the foreign exchange (forex) market. The strategy is based on research by Alina F. Serban, who adapted the concept from research in the equity market by Ronald J. Balvers and Yangru Wu. Serban creates a momentum factor using returns from the past three months and a mean reversion factor based on the deviation from the mean price. Regression analysis is used to predict the returns for the next month. The strategy is applied to the forex market, specifically the EURUSD, GBPUSD, USDCAD, and USDJPY pairs, with monthly rebalancing. The model's significance level and coefficients are similar to those in Serban's paper, but the returns and Sharpe Ratios obtained are not as good as claimed. The algorithm achieves a stable annual return of 11%, a 0.8 Sharpe Ratio, and an 11% drawdown. The strategy is centered on the theory of uncovered interest parity (UIP), which states that exchange rate changes should incorporate interest rate differentials. By identifying deviations from UIP, abnormal returns can be generated. The strategy uses the deviation from UIP as an indicator. The tutorial provides an overview of the interest parity conditions and the model and parameter estimation process.
Jing submitted the research Dual Thrust Trading Algorithm
Abstract
Abstract: The Dual Thrust trading algorithm, developed by Michael Chalek, is a popular strategy used in Futures, Forex, and Equity markets. This tutorial provides an introduction to the strategy and demonstrates how to implement it on QuantConnect. The algorithm calculates the range based on historical price data and opens positions when the market moves a certain range from the opening price. The strategy was tested on individual stocks in both trending and range-bound markets, with better results seen in trending markets. The strategy was also implemented on the S&P 500 ETF SPY, which outperformed the market. The strategy can be further extended to other markets by adjusting parameters and applying risk control.
Jing submitted the research Fundamental Factor Long Short Strategy
Abstract
The abstract for this QuantConnect discussion is about a long/short equity strategy based on fundamental factors. The strategy is based on the AQR white book and aims to consistently beat the market. The strategy uses three factors to rank stocks: value, quality, and momentum. Value is measured using the price-to-book ratio, quality is measured using the operation margin, and momentum is measured using the recent monthly return. The stocks are ranked based on these factors and assigned weights to calculate the final score. The strategy has been implemented and tested using an algorithm with 250 stocks. The abstract provides an overview of the strategy and its key components.
Jing submitted the research Can Crude Oil Predict Equity Returns
Abstract
Abstract: This tutorial explores the use of regression analysis to predict stock market returns by comparing them to short-term U.S. T-bill rates. The approach is based on the paper "Striking Oil: Another Puzzle?" by Gerben, Ben and Benjamin (2007). The strategy involves fully investing in stocks if the predicted return is higher than the risk-free rate, and investing in short-term U.S. T-bills if the predicted return is lower. The backtesting period is from 2010 to 2017, with monthly regression analysis and a rolling dynamic projection. The analysis shows that the strategy underperforms the market in recent years, potentially due to the weakening relationship between stocks and oil.
Jing submitted the research CAPM Alpha Ranking Strategy On Dow 30 Companies
Abstract
This abstract discusses the implementation of a CAPM Alpha Ranking Strategy on Dow 30 companies. The tutorial explains how to use historical data, set event handlers, conduct linear regression, and build custom functions in the QuantConnect Algorithm Lab. The strategy demonstrates that stocks that outperformed the market in the previous month are likely to outperform again in the following month. However, the model fails to capture alpha and performs poorly when market volatility increases. The CAPM theory is also explained, describing the relationship between systematic risk and expected return for assets. The formula for calculating the expected return of an asset given its risk is provided, along with an explanation of beta and alpha.
Jing submitted the research Intraday Dynamic Pairs Trading Using Correlation And Cointegration Approach
Abstract
This tutorial implements a high frequency and dynamic pairs trading strategy using a two-stage correlation and cointegration approach. The strategy is based on George J. Miao's work and is applied to the U.S. bank sector stocks. Backtesting with 10-minute stock data in September 2013 shows a compounding annual return of up to 26.924% and a 3.011 Sharpe ratio. This strategy is particularly profitable during market downturns and increased volatility. The example provides a basic starting point and can be customized with different lookback periods, data intervals, and threshold values. High Frequency Trading (HFT) and statistical arbitrage concepts are also discussed. The data used in the strategy consists of stocks in a specific industry to increase the number of pairs with high correlation.
Jing submitted the research Short Term Reversal
Abstract
The short-term reversal strategy aims to take advantage of the phenomenon where stocks with low returns in the past month or week tend to have positive abnormal returns in the following month or week, while stocks with high returns tend to have negative abnormal returns. To implement this strategy, the universe selection API is used to choose stocks with a price higher than 4, and the top 100 stocks are selected based on dollar volume. In the fine universe selection, the top 20 stocks are chosen based on market cap. The RateOfChange indicator is used to calculate the monthly return, and the strategy involves going long on the 10 stocks with the lowest performance in the previous month and going short on the 10 stocks with the highest performance from the previous month.
Jing submitted the research Forex Momentum
Abstract
This discussion focuses on the concept of Forex momentum, which is a trend following strategy that involves buying assets that have performed well in the past and selling assets that have performed poorly. The algorithm described in this discussion applies momentum to the Forex market, specifically focusing on 15 Forex pairs from 2006 to 2018. The algorithm selects the top 3 currencies with the strongest 12-month momentum against USD to go long, and the bottom 3 currencies with the lowest 12-month momentum against USD to go short. The discussion also references Quantpedia's FX Momentum as a resource for further information.
Jing submitted the research Sector Momentum
Abstract
In this discussion, the concept of sector momentum is explored as a strategy for reallocating capital based on changing market conditions. The algorithm presented is an adaptation of asset class momentum, where 10 sector ETFs are selected and the 3 with the strongest 12-month momentum are chosen for the portfolio, each weighted equally. The portfolio is then rebalanced monthly using a Scheduled Event. This approach is based on the idea that sectors with strong momentum are likely to continue performing well in the near future. The discussion references Quantpedia's research on sector momentum as a resource for further information.
Jing submitted the research Asset Class Trend Following
Abstract
Asset class trend following is a strategy that aims to exploit momentum anomalies across different assets. By using moving averages or momentum filters, this strategy seeks to gain exposure to an asset class when there is a higher probability of outperformance with lower risk. This algorithm applies trend following principles to 5 ETFs representing different asset classes such as stocks, bonds, and commodities. It uses a simple moving average to detect the trend and allocates equally to the ETFs when the closing price is above its ten-month moving average. Otherwise, it stays in cash. The implementation in LEAN includes a warm-up period of ten months to initialize the indicator for use in the algorithm.
Jing submitted the research Volatility Effect In Stocks
Abstract
This discussion focuses on the volatility effect in stocks, specifically how stocks with lower volatility tend to earn higher risk-adjusted returns compared to those with higher volatility. The study extends to US stocks with higher market capitalization. The method used involves universe selection, where stocks without fundamental data and those with a price below 5 are excluded. A universe of 100 stocks is selected based on dollar volume, and then 50 stocks with the highest market cap are chosen. The volatility is calculated using a RollingWindow to store daily return data, and the standard deviation is used as a measure of volatility. The trading strategy involves going long on 5 stocks with the lowest volatility and liquidating stocks not in the lowest volatility list. The portfolio is rebalanced monthly.
Jing submitted the research Asset Class Momentum
Abstract
This discussion focuses on the concept of asset class momentum and its application in a trend following algorithm. The algorithm uses the momentum effect to identify entry points by calculating the rate of change in price movements for a specific asset. The algorithm's portfolio consists of 5 ETFs from different asset classes, and it uses the MomentumPercent indicator in LEAN with a 12-month period. The algorithm selects the 3 ETFs with the strongest 12-month momentum percent and weights them equally in the portfolio. The portfolio is held for 1 month before being rebalanced with new momentum percent values. Unlike traditional asset class trend following strategies, this rotational momentum system compares the performance of different asset classes and selects only the best-performing assets for the portfolio. The portfolio is rebalanced monthly, ensuring that only the best-performing assets are held.
Jing submitted the research Pairs Trading With Stocks
Abstract
Pairs trading is a popular algorithmic trading strategy that involves finding two stocks that historically move together in price. This strategy takes advantage of mean reversion, where prices that have diverged from their historical relationship are expected to revert back to their mean. The first step is to select stock pairs from a universe of stocks by minimizing the sum of squared deviations between their normalized price series. The top 4 pairs with the smallest historical distance measure are then traded. The trading period is six months, and positions are opened when the price spread between the stocks diverges by two standard deviations. The position is closed when prices revert back. This strategy presents a statistical arbitrage opportunity and has been extensively studied in quantitative finance.
Jing submitted the research Forex Carry Trade
Abstract
Abstract: The forex carry trade strategy involves selling low-interest rate currencies and buying high-interest rate currencies to profit from the interest rate differential. This discussion focuses on implementing the strategy using custom data imported from Nasdaq Data Link. The trading universe consists of 9 currencies with available central bank interest rate data. The algorithm sorts the forex symbols based on interest rates and goes long on the currency with the highest interest rate while going short on the currency with the lowest interest rate. The strategy is rebalanced monthly using the Scheduled Event method. This discussion serves as a reference for implementing the forex carry trade strategy.
Jing submitted the research Momentum Effect In Stocks
Abstract
This abstract discusses the momentum effect in stocks and presents an algorithm that explores this phenomenon in large-cap stocks. The momentum anomaly suggests that stocks that have performed well in the recent past are likely to continue performing well in the near future. The algorithm uses the universe selection API to create a momentum portfolio, eliminating stocks with a price lower than $5 and ETFs without fundamental data. The fine-universe selection chooses the 50 largest companies based on market capitalization. The momentum percent is calculated as the relative difference in stock prices over a certain period. The abstract concludes by referencing Quantpedia's article on the momentum effect in stocks.
Jing submitted the research Momentum Effect In Country Equity Indexes
Abstract
This discussion examines the momentum effect in country index exchange-traded funds (ETFs). The algorithm selects 35 country index ETFs as the trading universe and uses the MomentumPercent indicator helper method to save the indicator of each symbol. At the start of each month, the algorithm selects the top five index ETFs with the best 6-month momentum to open long positions and liquidates ETFs that are no longer in the top list. The algorithm's results show that momentum effects exist in country indices. Holding a portfolio of the five best performing country index ETFs over the previous six months outperformed the equal-weighted portfolio by around 90% per annum from 2002 to 2022.
Jing started the discussion Algorithm Examples
Hi QC Community Members,
Jing left a comment in the discussion Simulation of options exercise is messing up the results
At the expiration date, an open option position will be exercised or assigned automatically if...
Jing left a comment in the discussion Is there a time or memory limit in how long the OnData function can be?
It depends on your algorithm logic. OnData () method is called in the data resolution. If you only...
Jing left a comment in the discussion Heikinashi bars constructed from trade ticks
We support Heikinashi(symbol, resolution) indicator.
Jing left a comment in the discussion Error not in index
You initialize self.position as None in Initialize(), so self.position is always None before you...
Jing left a comment in the discussion How to generate the Rolling Window of 4-TradeBars
To obtain the bar open price in OnData(self, data),
Jing started the discussion A tool algorithm for picking effective factors in stock selection model
The most important feature of a successful multi factor stock selection model is its ability to...
Jing started the discussion Generate volatility surface plot by interpolation
Hi everyone,
QuantConnect™ 2024. All Rights Reserved
Jing left a comment in the discussion Algo Framework ETF Momentum Rebalancing using Mean Variance Optimization
andrew_czeizler, this is an old post. Now we've already put...
6 years ago