#StrategyLibrary

In the latest addition to the QuantConnect #StrategyLibrary we use regression on the Fama-French factors to predict future returns following the research published by David Blitz & Martin Martens from @Robeco and Joop Huij from @RSMErasmus , a thread (1/7):

In the newest addition to the QuantConnect #StrategyLibrary, we use Leveraged ETFs with Systematic Risk Management using research published by Michael A. Gayed @leadlagreport and Charlie Bilello @charliebilello (1/6)

In the newest addition to the QuantConnect #StrategyLibrary, @ikhlaas_gurrib from @CUDUAE finds applying an Ichimoku Cloud breakout strategy to a basket of the largest energy stocks leads to a portfolio that greatly outperforms buying-and-holding the same basket of stocks. (1/5)

In this strategy in the QC #StrategyLibrary, we employ the dual thrust trading algorithm developed by @WeTradeFutures. The idea of Dual Thrust is similar to a typical breakout system, but this strategy is helpful because it automatically adjusts based on recent volatility. (1/5)

In the newest addition to the QuantConnect #StrategyLibrary, we look at applying a gradient boosting model to forecast intraday price changes when given a collection of technical indicator values. (1/6)

In the newest addition to the QuantConnect #StrategyLibrary, we train Gaussian Naive Bayes classifiers to forecast the future direction of the largest technology stocks when given the historical returns of the technology sector constituents. (1/6)

In the newest addition to the QuantConnect #StrategyLibrary, We apply 7 factors that involve Fundamental data to narrow down a universe of technology stocks. (1/6)

In this addition to the QuantConnect #StrategyLibrary, we use the news sentiment of drug manufacturers to predict the intraday moves of their stock prices. Our research follows that of Isah, Shah, & Zulkernine from @queensu. (1/5)

In the newest addition to the QuantConnect #StrategyLibrary, we model a Pairs Trading Process as an Ornstein-Uhlenbeck Process to derive the Optimal Entry and Liquidation values. (1/6)

In the newest addition to the QuantConnect #StrategyLibrary, Lei Gao of @IowaStateU, Yufeng Han of @unccharlotte, @SophiaZhengziLi of @RutgersBSchool, and Guofu Zhou of @WUSTLbusiness show direction trends of some actively-traded ETFs. (1/5)

In the newest addition to the QuantConnect #StrategyLibrary, We apply a Convolutional Neural Network, which is often used for Image Recognition, along with a method of temporally sectioning the data within the Neural Network layers. (project credit: @BigDataFinance) (1/7)

In the latest addition to the QuantConnect #StrategyLibrary, Travis Sapp from @IowaStateU found that the historical returns of a mutual fund and the nearness of its net asset value to a previous high can provide significant predictive power about the fund's future returns. (1/5)

In the newest addition to the QuantConnect #StrategyLibrary we apply an SVM to components of time-series data transformed by Wavelet Decomposition in an attempt to forecast EURJPY. (1/7)

In the newest addition to the QuantConnect #StrategyLibrary, we look at intraday arbitrage between index ETF’s. (1/6)