ARIMA Modeling
If we combine differencing with autoregression and a moving average model, we obtain a non-seasonal ARIMA model. ARIMA is an acronym for AutoRegressive Integrated Moving Average model. The term...
View ArticleSimulate White Noise (WN) in R
The function arima.sim() can be used to simulate data from a variety of time series models. Based on the model we want to apply, we specify the appropriate values for p, d and q to the model...
View ArticleSimulate Random Walk (RW) in R
When a series follows a random walk model, it is said to be non-stationary. We can stationarize it by taking a first-order difference of the time series, which will produce a stationary series, that...
View ArticleAutoRegressive (AR) Model in R
AutoRegressive (AR) model is one of the most popular time series model. In this model, each value is regressed to its previous observations. AR(1) is the first order autoregression meaning that the...
View ArticleEstimating AutoRegressive (AR) Model in R
We will now see how we can fit an AR model to a given time series using the arima() function in R. Recall that AR model is an ARIMA(1, 0, 0) model. We can use the arima() function in R to fit the AR...
View ArticleForecasting with AutoRegressive (AR) Model in R
Now that we know how to estimate the AR model using ARIMA, we can create a simple forecast based on the model. Step 1: Fit the model The first step is to fit the model as ARIMA(1, 0, 0). We have...
View ArticleMoving Average (MA) Model in R
A Moving Average is a process where each value is a function of the noise in the past observations. These are the random error terms which follow a white noise process. The general form is MA(q), where...
View ArticleForecasting with ARIMA Modeling in R – Case Study
In this lesson, we will take a new dataset (stock prices) and use all that we have learned to create a forecast using the ARIMA Models. We will take the closing prices of Facebook stock for this...
View ArticleAutomatic Identification of Model Using auto.arima() Function in R
auto.arima() Function R also has a package called forecast, which contains many forecasting functions for time series and linear models. It also contains a very useful function called auto.arima, which...
View ArticleFinancial Time Series in R – Course Conclusion
This course provided an overview of the fundamentals of time series analysis and how we can perform time series analysis in R. We reviewed some of the most important concepts of time series analysis...
View ArticleIntroduction to Quantitative Trading
Quantitative trading involves developing and executing trading strategies based on quantitative research. The quants traders start with a hypothesis and then conduct extensive data crunching and...
View ArticleQuantitative Trading – Advantages and Disadvantages
Advantages Quantitative trading has many advantages over the discretionary approach of trading. The performance of a quantitative strategy can be tested with historical market data. This process is...
View ArticleTypes of Quantitative Trading Strategies
There are different types of trading strategies which differ in terms of their time horizon, risk profiles, capital requirements, as well as liquidity and volatility needed for a correct execution....
View ArticleMomentum Strategies
Even though we classify momentum with a longer time frame than a day, it is necessary to point out that momentum can also exist within the day. Traders can find momentum during the day, as well as for...
View ArticleMean Reversion Strategies
Mean reversion strategies, also called pairs trading, tend to capture market anomalies or inefficiencies between prices of stocks, ETFs or commodities with similar behavior. These assets usually...
View ArticleMarket Making Strategies and Day Trading Strategies
Market Making Strategies Market making strategies are called execution strategies or sell-side methods which are designed to capture spreads, otherwise known as the difference in price between buys...
View ArticleHow to Generate Trading Ideas
As a quantitative trader, it is a good practice to establish a strategy pipeline that will provide with a stream of ongoing trading ideas. We can have a framework to backtest trading ideas that can be...
View ArticleDesigning A Trading Strategy For Profit
The first step in the process of designing a trading strategy is to choose which type of strategy will be used and a timeframe to trade. As stated earlier, the strategy can seek different goals and...
View ArticleBacktesting a Trading Strategy – Considerations
Backtesting a trading strategy refers to testing the strategy with historical data and observe their metrics, results and performance. Commonly, backtesting has some pitfalls that should be considered...
View ArticleRisk Management of a Trading Strategy
Lastly we should have rules to manage the risk of the strategy. Risk and money management are the most important aspect in a trading strategy. This field focuses on drawdowns, leverage and volatility....
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