At the end of my last blog, I had asked a few questions. Now, I will answer them all at the same time. But before we go ahead, please use a fix to fetch the data from Google to run the code below. Trading Using Machine Learning In Python Part-2 Click To Tweet. This was the first question I had asked. To know if your data is overfitting or not, the best way to test it would be to check the prediction error that the algorithm makes in the train and test data. The other day I was reading an article on how AI has progressed so far and where it is going. I was awestruck and had a hard time digesting the picture the author drew on possibilities in the future. And here is one of the possibilities where AI could be applied in trading field, para from the article. So it would be as if one of her fingers was a scalpel and she could do the surgery without holding any tools, giving her much finer control over her incisions. Strategy inexperienced surgeon performing a tough operation could bring a couple of her mentors into the scene as she operates to watch her work through her eyes and think instructions or advice to her. You can read the article here. At this moment, AI and Machine Learning have already progressed enough and they can predict stock prices with a great level of accuracy. Let me show you how. Machine Learning in Trading How to Predict Stock Prices using Regression? Hence, as a quant, one is always on a look out for good trading ideas. One of the good resources for trading strategies that have been gaining wide popularity is the Quantpedia site. Quantpedia has thousands of financial research papers that can be utilized to create profitable trading strategies. Quantpedia has made some of these trading strategies available for free to their users. The Quantpedia page for this trading strategy provides a detailed description which includes the weeks high effect explanation, source research paper, other related papers, a visualization of the strategy performance and also other related trading strategies. When stock prices are near the week high, investors are unwilling to bid the price quant the way to the fundamental value. Jordan, and Mark H. The financial paper says that traders use the week high as a reference point which they evaluate the potential impact of news against. The information eventually prevails and the price moves up, resulting in a continuation. It works similarly for week lows. The trading strategy developed by the authors buys stocks in industries in which stock prices are close to week highs and shorts stocks in industries in which stock prices are far from week highs. They found that the industry week high trading strategy is more profitable than the individual week high trading strategy proposed by George and Hwang Having understood the weeks High Effect, we will try to backtest a simple trading strategy using R programming. Please note that we are not trying to replicate the exact trading strategy developed by the authors in their research paper. We test our trading strategy for a 3-year backtest period using daily data on around stocks listed on the National Stock Exchange of India Ltd. Brief about the strategy The trading strategy reads the daily historical data for each stock in the list and checks if the price of the stock is near its week high at the start of each month. We have shown how to check for this condition in step 4 of the trading strategy formulation process illustrated below. For all the stocks that pass this condition, we form an equal weighted portfolio for that month. We take a long position in these stocks at the start of the month and square off our position at the start of the next month. We follow this process for every month of our backtest period. Finally, we compute and chart the performance metrics of our trading strategy. Now, let us understand the process of trading strategy formulation in a step-by-step manner. For reference, we have posted the R code snippets of relevant sections of the trading strategy under its respective step. First, we set the backtest period, and the upper and lower thresholds values for determining whether a stock is near its week high. In this step, we read the historical stock data using the read. Since we are using the daily data we need to determine the start date of each month. The start date need not necessarily be the 1 st of every month because the 1 st can be a weekend or a holiday for the stock exchange. Hence, we write an R code which will determine the first date of each month. Check if the stock is near the week high mark. In this part, we first compute the week high price for each stock. We then compute the upper and the lower thresholds using the week high price. If the stock price at the quant of the month falls in this range, we then consider the stock to be near its week high mark. We have also included one additional condition in the step. For all the stocks that fulfill the criteria mentioned in the step above, we create a long-only portfolio. The entry price equals the price at the start of the month. We square off our long position at the start of the next month. We consider the close price of the stock for our entry and exit trades. In this step, we write an R code which creates a summary sheet of all the trades for each month strategy the backtest period. A sample summary sheet has been shown below. In the final step, we compute the portfolio performance over the entire backtest period and also plot the equity curve using the PerformanceAnalytics package in R. The portfolio performance is saved in a CSV file. A sample summary of the portfolio performance has been shown below. In this case, the input strategy to our trading strategy were as follows:. As can be observed from the quant curve, our trading strategy performed well during quant initial period and then suffered drawdowns in the middle of the backtest period. The Sharpe ratio for the trading strategy comes to 0. This was a simple trading strategy that we developed using the week high effect explanation. One can tweak this trading strategy further to improve its performance and make it more robust or try it out on different markets. Machine Learning has many advantages. It is quant hot topic right now. I will explore one such model that answers this question in a series of blogs. Development of a successful algorithmic strategy is already a difficult endeavor. However trading a single strategy can pose its own set of risks, even if the strategy itself is robust and profitable. So how do we as algorithmic traders understand exactly what our systems are delivering, change our mindset from development to implementation, and increase our risk adjusted returns? Most traders are familiar with looking at standard performance reports which have statistics like CAGR, Sharpe Ratio, and max drawdown. But these single numbers only provide a small glimpse into what the system is actually delivering. By adding return distribution analysis to your tool kit, you will be able to have a better grasp about what the system may produce on a more granular level. The most common method for classifying a trading system is based on the entry type, trading a momentum or mean reversion style. This in the end is subjective and constraining, as many strategies will incorporate elements from both regimes. For example, a mean reversion strategy may employ the use of a filter that may have momentum characteristics. After this addition of the filter is it still a mean reversion system? By analyzing the skew, and looking at the tails of our return distribution we can get a much better indication of what the strategy is actually delivering. Thus allowing us to make a quantitative judgement as to which regime it belongs to. Most novice traders think of their strategies as standalone systems, maintaining the same concept from ideation to implementation. However, there are two distinct environments, the vacuum of the quantitative research laboratory, and the investment portfolio in which you will execute your strategy. We need to consider the implications of this implementation, and its effect on our current portfolio and the fit into our investment mandate. The best way to do that is to consider a strategy for allocation as an investable security. At its most fundamental level any strategy has a singular purpose. Which is to deliver a return series with particular characteristics, usually outsized risk adjusted returns. If this is the case, then we can consider a strategy that has been funded as making a long bet on that particular return series. This is the same as investing in any stock, commodity, or other asset. Now there is basically no difference in motivation between investing in your strategy and investing in any other asset or security. You will allocate the most funds to trading who exhibit the most desirable characteristics, and less to those who do not. If we can accept this logic that investing in completed strategies, and investing in any other asset is the same. Naturally, the next logical step would be to create a portfolio. No one would recommend their friend to buy only a single stock. So why would you as strategy systematic trader only want to have one strategy? We can now rely on two areas that have been heavily researched in academia and practiced in the field for many decades, portfolio optimization and diversification. By applying these very key principles that go into creating a portfolio of traditional assets, we can create a portfolio of multiple strategy systems. The same trading that you get from creating a portfolio of traditional assets, such as decreased equity curve volatility and increase risk adjusted returns, can be then transferred to your set of systematic trading strategies. You can click on the link provided above to access the recorded session of the webinar. Asset return prediction is difficult. One significant reason is that time series analysis TSA models require your data to be stationary. This renders traditional models mostly ineffective for our purposes. There are many algorithms to choose from, but few are flexible enough to address the challenges of predicting asset returns:. To recap, we need a model framework that is flexible enough to 1 adapt to non-stationary processes and 2 provide a reasonable approximation of the non-linear process that is generating the data. Markov models These are used to model trading where the future state depends only on the current state and not any past states. Hidden Markov models Used to model processes where the true state is unobserved hidden but there are observable factors that give us useful information to guess the true state. Expectation-Maximization E-M This is an algorithm that iterates between computing class parameters and maximizing the likelihood of the data given those parameters. An easy way quant think about applying mixture models to asset return prediction is to consider asset returns as a sequence of states or regimes. Each regime is characterized by its own descriptive statistics including mean and volatility. Example regimes could include low-volatility and high-volatility. We can also assume that asset returns will transition between these regimes based on probability. The underlying model assumption is that each regime is generated by a Gaussian process with parameters we can estimate. Under the hood, GMM employs an expectation-maximization algorithm to estimate regime parameters and the most likely sequence of regimes. GMMs are flexible, generative models that have had success approximating non-linear data. Generative models are special in that they try to mimic quant underlying data process such that trading can create new data that should look like original data. In recent years, machine learning has been generating a lot of curiosity for its profitable application to trading. Researchers have found that some models have more success rate compared to other machine learning models. In this post, we will cover the basics of XGBoost, a winning model for many kaggle competitions. Developed by Tianqi Chen, the eXtreme Gradient Boosting XGBoost model is an implementation of the gradient boosting framework. Gradient Boosting algorithm is a machine learning technique used for building predictive tree-based models. An Introduction to Decision Trees. Boosting is an ensemble technique in which new models are added to correct the errors made by existing models. Models are added sequentially until no further improvements can be made. The ensemble technique uses the tree ensemble model which is a set of classification and regression trees CART. The ensemble approach is used because a single CART, usually, does not have a strong predictive power. By using a set of CART i. Gradient boosting is an approach where new models are created that predict the residuals or errors of prior models and then added together to make the final prediction. The loss function L which needs to be optimized can be Root Mean Squared Error for regression, Logloss for binary classification, or mlogloss for multi-class classification. It is called gradient boosting because it uses a gradient descent algorithm to quant the loss when adding new models. The Gradient boosting algorithm supports both regression and classification predictive modeling problems. We install the xgboost library using the install. To load this package we use the library function. We also load other relevant packages required to run the code. The indicators computed include Relative Strength Index RSIAverage Directional Index ADXand Parabolic SAR SAR. We create a lag in the strategy indicators to avoid the look-ahead bias. This gives us our input features for building the XGBoost model. Since this is a sample model, we have included only a few indicators to build our set of input features. This makes it a binary classification problem. We compute the daily price change and assigned a positive 1 if the daily price change is positive. If the price change is negative, we assign a zero value. Combine the input features into a matrix The input features and the target variable created in the above step are combined to form a single matrix. We use the matrix structure in the XGBoost model since the xgboost library allows data in the matrix format. Split the dataset into training data and test data In the next step, we split the dataset into training and test data. Using this training and test dataset we create the respective input features set and the target variable. We use the xgboost function to train the model. The arguments of the xgboost function are shown in the picture below. The data argument in the xgboost function is for the input features dataset. It accepts a matrix, dgCMatrix, or local data file. The nrounds argument refers to the max number of iterations i. The obj argument refers to the customized objective function. It returns gradient and second order gradient with given prediction and dtrain. We can also use the cross-validation function of xgboost i. In this case, the original sample is randomly partitioned into nfold equal size subsamples. The cross-validation process is then repeated nrounds times, with each of the nfold subsamples used exactly once as the validation data. Output The xgb. To make predictions on the unseen data set i. We have to, therefore, perform a simple transformation before we are able to use these results. The threshold value can be changed depending upon the objective of the modeler, the metrics e. F1 score, Precision, Recall that the modeler wants to track and optimize. Different evaluation metrics can be used to measure the model performance. It compares the predicted score with the threshold of 0. If the predicted score is less than 0. If this value is not equal to the actual result from the test data set, then it is taken as a wrong result. The code for measuring the performance is given below. Alternatively, we can use hit rate or create a confusion matrix to measure the model performance. Finally, we can plot the XGBoost trees using the xgb. If NULL, all trees of the model are plotted. Readers can catch some of our previous machine learning blogs links given below. We will be covering more machine learning concepts and techniques in our coming posts. Predictive Modeling in R for Algorithmic Trading Machine Learning and Its Application in Forex Markets. Time series data is simply a collection of observations generated over time. For example, the speed of a race car quant each second, daily temperature, weekly sales figures, stock returns per minute, etc. A time series can be generated for any variable that is changing over time. Time series analysis comprises of techniques for analyzing time series data in an attempt to extract useful statistics and identify characteristics of the data. Time series forecasting is the use of a mathematical model to predict future values based on previously observed values in the time series data. The graph shown below represents the daily closing price of Aluminium futures over a period of 93 trading days, which is a time series. Mean reversion is the theory which suggests that prices, returns, or various economic indicators tend to move to the historical average or mean over time. This theory has led to many trading strategies which involve the purchase or sale of a financial instrument whose strategy performance has greatly differed from quant historical average without any apparent reason. For example, let the price of gold increase on average by INR 10 every day and one day the price of gold increases by INR 40 without any significant news or factor behind this rise, then by the mean reversion principle we can expect the price of gold to fall in the coming days such that the average change in price of gold remains the same. In such a case, the mean reversionist would sell gold, speculating the price to fall in the coming days. Thus, making profits by buying the same amount of gold he had sold earlier, now at a lower price. A mean-reverting time series has been plotted below, the horizontal black line represents the mean and the blue curve is the time series which tends to revert back to the mean. A collection of random variables is defined to be a stochastic or random process. A stochastic process is said to be stationary if its mean and variance are time invariant constant over time. A stationary time series will be mean reverting in strategy, i. A stationary time series will not drift too far away from its mean because of its finite constant variance. A non-stationary time series, on the contrary, will have a time varying variance or a time varying mean or both, and will not tend to revert back to its mean. In the financial industry, traders take advantage of stationary time series by placing orders when the price of a security deviates trading from its historical mean, speculating the price to revert back to its mean. They start by testing for stationarity in a time series. Financial data points, such as prices, are often non-stationary, i. Non-stationary data quant to be unpredictable and cannot be modeled or forecasted. A non-stationary time series can be converted into a stationary time series by either differencing or detrending the data. A random walk the movements of an object or changes in a variable that follow no discernible pattern or trend can be transformed into a stationary series by differencing computing the difference between Y t and Y t The disadvantage of this process is strategy it results in losing one observation each time the difference is computed. A non-stationary time series with a deterministic trend can be converted into a stationary time series by detrending removing the trend. Detrending does not result strategy loss of observations. A linear combination of two non-stationary time series can also result in a stationary, mean-reverting time series. The time series integrated of at least order 1which can be linearly combined to result in a stationary time series are said to be cointegrated. One of the simplest mean reversion related trading strategies is to find the average price over a specified period, followed by determining a high-low range around the average value from where the price tends to revert back to the mean. The trading signals will be generated when these ranges are crossed placing a sell order when the range is crossed on the upper side and a buy order when the range is crossed on the lower side. The trader takes contrarian positions, i. This strategy looks too good to be true and it is, it faces severe obstacles. This strategy would result in losses if such a situation arises. Pairs Trading is another strategy that relies on the trading of mean reversion. Two co-integrated securities are identified, the spread between the price of these securities would be stationary and hence mean reverting in nature. An extended version of Pairs Trading is called Statistical Arbitrage, where many co-integrated pairs are identified and split into buy and sell baskets based on the spreads of each pair. The first step in a Pairs Trading or Stat Arb model is to identify a pair of co-integrated securities. One of the commonly used tests for checking co-integration between a pair of securities is the Augmented Dickey-Fuller Test ADF Test. It tests the null hypothesis of a unit root being present in a time series sample. A time series which has a unit root, i. The augmented Dickey-Fuller statistic, also known as t-statistic, is a negative number. The more negative it is, the stronger the rejection of the null hypothesis that there is a unit root at some level of confidence, which would imply that the time series is stationary. The t-statistic is compared with a critical value parameter, if the t-statistic is less than the critical value parameter then the test is positive and the null hypothesis is rejected. We start by importing relevant libraries, followed by fetching financial data for two securities using the quandl. Quandl provides financial and economic data directly in Python by importing the Quandl library. In this example, we have fetched data for Aluminium and Lead futures from MCX. We then print the first five rows of the fetched data using the head function, in order to view the data being pulled by the code. Next, using the statsmodels. This array contains values like the t-statistic, p-value, and critical value parameters. Here, we consider a significance level of 0. Statistics behind pairs trading https: ADF test using excel https: Many of you must have come across this famous quote by Neils Bohr, a Danish physicist. Prediction is the theme of this blog post. In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and demonstrate a step-by-step process of ARIMA modeling using R programming. Forecasting involves predicting values for a variable using its historical data points or it can also involve predicting the change in one variable given the change in the value of another variable. Forecasting approaches are primarily categorized into qualitative forecasting and quantitative forecasting. Time series forecasting falls under the category of quantitative forecasting wherein statistical principals and concepts are applied to a given historical data of a variable to forecast the future values of the same variable. Some time series forecasting techniques used include:. ARIMA stands for Autoregressive Integrated Moving Average. ARIMA is also known as Box-Jenkins approach. Box and Jenkins claimed that non-stationary data can be made stationary by differencing the series, Y t. We will follow the steps enumerated below to build our model. To model a time series with the Box-Jenkins approach, the series has to be stationary. A stationary time series means a time series without trend, one having a constant mean and variance over time, which makes it easy for predicting values. Testing for stationarity We test for stationarity using the Augmented Dickey-Fuller unit root test. The p-value resulting from the ADF test has to be less than 0. If the p-value is greater than 0. Differencing To convert a non-stationary process to a stationary process, we apply the differencing method. Differencing a time series means finding the differences between consecutive values of a time series data. The differenced values form a new time series dataset which can be tested to uncover new correlations or other interesting statistical properties. We apply the appropriate differencing order d to make a time series stationary before we can proceed to the next step. In this step, we identify the appropriate order of Autoregressive AR and Moving average MA processes by using the Autocorrelation function ACF and Partial Autocorrelation function PACF. For AR models, the ACF will dampen exponentially and the PACF will be used to identify the order p of the AR model. If we have one significant spike at lag 1 on the PACF, then we have an AR model of the order 1, i. If we have significant spikes at lag 1, 2, and 3 on the PACF, then we have an AR model of the order 3, i. For MA models, the PACF will dampen exponentially and the ACF plot will be used to identify the order of the MA process. If we have one significant spike at lag 1 on the ACF, then we have an MA model of the order 1, i. If we have significant spikes at lag 1, 2, and 3 on the ACF, then we have an MA model of the order 3, i. Once we have determined the parameters p,d,q we estimate the accuracy of the ARIMA model on a training data set and then use the fitted model to forecast the values of the test data set using a forecasting function. In the end, we cross check whether our forecasted values are in line with the actual values. Now, let us follow the steps explained to build an ARIMA model in R. There are a number of packages available for time series analysis and forecasting. We load the relevant R package for time series analysis and pull the stock data from yahoo finance. In the next step, we compute the logarithmic returns of the trading as we want the ARIMA model to forecast the log returns and not the stock price. We also plot the log return series using the plot function. Next, we call the ADF test on the returns series data to check for stationarity. The p-value of 0. If the series were to be non-stationary, we would have first differenced the returns series to make it stationary. In the next step, we fixed a breakpoint which will be used to split the returns dataset in two parts further down the code. We truncate the original returns series till the breakpoint, and call the ACF and PACF functions on this truncated series. We can observe these plots and arrive at the Autoregressive AR order and Moving Average MA order. We know that for AR models, the ACF will dampen exponentially and the PACF plot will be used to identify the order p of the AR model. For MA models, the PACF will dampen exponentially and the ACF plot will be used to identify the order q of the MA model. Thus, our ARIMA parameters will be 2,0,2. Our objective is to forecast the entire returns series from breakpoint onwards. We will make use of the For Loop statement in R and within this loop we will forecast returns for each data point from the test dataset. In the code given below, we first initialize a series which will store the actual returns and another series to store the forecasted returns. In the For Loop, we first form the training dataset and the test dataset based on the dynamic breakpoint. We call the arima function on the training dataset for which the order specified is 2, 0, 2. We use this fitted model to forecast the next data point by using the forecast. One can use the confidence level argument to enhance the model. We will be using the forecasted point estimate from the model. We can use the summary function to confirm the results of the ARIMA model are within acceptable limits. In the last part, we append every forecasted return and the actual return to the forecasted returns series and the actual returns series respectively. Before we strategy to the last part of the code, let us check the results of the ARIMA model for a sample data point from the test dataset. The standard error is given for the coefficients, and this needs to be within the acceptable limits. The Akaike information criterion AIC score is a good indicator of the ARIMA model accuracy. Lower the AIC score better the model. We can also view the ACF plot of the residuals; a good ARIMA model will have its autocorrelations below the threshold limit. The forecasted point return is Let us check the accuracy of the ARIMA model by comparing the forecasted returns versus the actual returns. The last part of the code computes this accuracy information. If the sign of the forecasted return equals the sign of the actual returns we have assigned it a positive accuracy score. One can try running the model for other possible combinations of p,d,q or instead use the auto. To conclude, in this post we covered the ARIMA model and applied it for forecasting stock price returns using R programming language. We also crossed checked our forecasted results with the actual returns. The performance of a trading strategy is measured with a set of parameters. For example, if you are trading in equity then your returns are compared against the benchmark index. The consistency of returns of the strategy also proves to be a significant factor. Trading Using Machine Learning In Python Part On June 19, By admin In Getting StartedPythonTrading Strategies 1 Comment. By Varun Divakar Continued: On June 12, By admin In Getting StartedOther LanguagesProgramming and Trading ToolsTrading Strategies 0 Comment. By Sushant Ratnaparkhi The other day I was reading an article on how AI has progressed so far and where it is going. Here is how I reacted. On May 8, By admin In Programming and Trading ToolsR ProgrammingTrading Strategies 0 Comment. What is Weeks High Effect? Framing our Weeks High Effect Strategy using R programming Having understood the weeks High Effect, we will try to backtest a simple trading strategy using R programming. Run the program for each stock in the list for s in 1: In this case, the input parameters to our trading strategy were as follows: Plotting the Equity Curve As can be observed from the equity curve, our trading strategy performed well during the initial period and then suffered drawdowns in the middle of the backtest period. On May 5, By admin In Getting StartedPythonTrading Strategies 1 Comment. By Varun Divakar Introduction Machine Learning has many advantages. On April 28, By admin In Trading Strategies 0 Comment. Distribution Analysis of Trading Strategies Most traders are familiar with looking at standard performance reports which have statistics like CAGR, Sharpe Ratio, and max drawdown. Strategies as investable securities, changing your mindset. Applying portfolio optimization and diversification. On April 20, By admin In Programming and Trading ToolsPythonTrading Strategies 0 Comment. By Brian Christopher Asset return prediction is difficult. That presents a problem. What are our options? There are many algorithms to choose from, but few are flexible enough to address the challenges of predicting asset returns: Can Mixture Models offer a solution? First, they are based on several well-established concepts. The most common is the Gaussian mixture model GMM. On April 5, By admin In Programming and Trading ToolsR ProgrammingTrading Strategies 2 Comments. By Milind Paradkar In recent years, machine learning has been generating a lot of curiosity for its profitable application to trading. Basics of XGBoost and related concepts Developed by Tianqi Chen, the eXtreme Gradient Boosting XGBoost model is an implementation of the gradient boosting framework. The objective of the XGBoost model is given as: Install and load the xgboost library We install the xgboost library using the install. Cross-validation We can also use the cross-validation function of xgboost i. Make predictions on the test data To make predictions on the unseen data set i. View the trees from a model xgb. Download Data Files ACC. On March 16, By admin In Getting StartedPythonTrading Strategies 0 Comment. By Devang Singh Time series data is simply a collection of observations generated over time. Mean Reversion Mean reversion is the theory which suggests that prices, returns, or various economic indicators tend to move to the historical average or mean trading time. Trading Strategies based on Mean Reversion One of the simplest mean reversion related trading strategies is to find the average price over a specified period, followed by determining a high-low range around the average value from where the price tends to revert back to the mean. Co-integration check ADF Test Consider the Python code shown below for checking co-integration: Other Links Statistics behind pairs trading https: On March 9, By admin In Programming and Trading ToolsR ProgrammingTrading Strategies 2 Comments. What is a forecasting model in Time Series? Some time series forecasting techniques used include: Autoregressive Models AR Moving Average Models MA Seasonal Regression Models Distributed Lags Models What is Autoregressive Integrated Moving Average ARIMA? Differencing I-for Integrated This involves differencing the time series data to remove the trend and convert a non-stationary time series to a stationary one. Testing and Ensuring Stationarity To model a time series with the Box-Jenkins approach, the series has to be stationary. Identification of p and q In this step, we identify the appropriate order of Autoregressive AR and Moving average MA processes by using the Autocorrelation function ACF and Partial Autocorrelation function PACF. Identifying the p order of AR model For AR models, the ACF will dampen exponentially and the PACF will be used to identify the order p of the AR model. Identifying the q order of MA model For MA models, the PACF will dampen exponentially and the ACF plot will be used to identify the order of the MA process. Estimation and Forecasting Once we have determined the parameters p,d,q we estimate the accuracy of the ARIMA model on a training data set and then use the fitted model to forecast the values of the test data set using a forecasting function. Building ARIMA model using R programming Now, let us follow the steps explained to build an ARIMA model in R. NS[,4] In the next step, we compute the logarithmic returns of the stock as we want the ARIMA model to forecast the log returns and not the stock price. Conduct ADF test on log returns series print adf. From the coefficients obtained, the return equation can be written as: Conclusion To conclude, in this post we covered the ARIMA model and applied it for forecasting stock price returns using R programming language. On January 5, By admin In Programming and Trading ToolsR ProgrammingTrading Strategies 0 Comment. Categories Career Advice 9 Downloadables 15 Getting Started 74 News 44 Events 28 Press Releases 3 Programming and Strategy Tools 73 Other Languages 10 Python 24 R Programming 35 Trading Platforms 5 Project Work EPAT 10 Trading Strategies 55 Webinars 26 Previous Webinars 25 Upcoming Webinars 1. Helpful Sources Quantocracy Quantsportal Quantpedia KDnuggets R-bloggers The Financial Hacker Wall Street Oasis Robot Wealth Turing Finance. India QuantInsti Trading Learning Pvt Ltd A, Boomerang, Chandivali Farm Road, Powai, Mumbai Toll Free: Connect with us Show us some love on Quantocracy. Click here to register.
How quant trading strategies are developed and tested w/ Ernie Chan
How quant trading strategies are developed and tested w/ Ernie Chan
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