# Blog

AI, Quantitative Analysis and Data Science Solutions for Finance and Manufacturing.

### Quant Basics 7: Identify Best Parameters

In the previous post we used unsupervised machine learning to identify the best cluster of train-test results. The question is: is that cluster meaningful? If the parameters of the moving averages for the cluster are scattered evenly over the parameter space, this cluster may not be very meaningful. On the other hand, if the parameters…

In the previous section we ran a parameter sweep over the train and the test set of our strategy and looked at the average PnL. In this post we will start with system parameter permutation (SPP) in order to improve the performance of our system without falling prey to data mining bias. Remember that we…

We will explore Long Short-Term Memory Networks (LSTM networks) because this deep learning technique can be helpful in sequential data such as time series. As its name suggests, it can ‘remember’ previous observations, which wouldn’t be so necessary in non-sequential data, but especially helpful for time series data like the financial markets. This is the…

### Quant Basics 5: Parameter Sweep

Introduction In the last section we ran a single backtest. However, for our strategy to work we should optimise our strategy parameters. If we blindly run through a large set of parameters and then pick the best one we are very likely to fall for an issue called Data Mining Bias. This means that if…

### Quant Basics 4: Analysing A Single Backtest

In the previous posts we have downloaded market data, developed a vectorised backtest and calculated PnL, Sharpe ratio and drawdown. In this post we will set up, run and analyse a single backtest. This is the basis for running parameter sweeps and optimisations with hundreds or thousands of backtests. So, let’s set some important parameters…

### Quant Basics 3: Sharpe and Drawdown

Sharpe Ratio In the previous sections of Quant Basics we looked at producing data sources and how to write a vectorised backtest. We also calculated our first metric – PnL and tested its functionality. In this section we will add two more metrics that are very important for strategy evaluation: Sharpe ratio and drawdown. Let’s…

### Quant Basics 2: Vectorised Backtest

Why Vectorise? There are several ways to backtest a strategy on historical data. In this section we demonstrate vectorisation. This and the previous section will serve as preliminary exercises before we dive deeply into the quantitative section. However, one should not underestimate the pitfalls of backtesting. It is very easy to make mistakes here, so…

### Quant Basics 1: Data Sources

Introduction Welcome to the Quant Basics series. This mini series came from the observation that most people starting in quantitative trading focus almost entirely on the generation of trading signals. While this is important, several other areas in quantitative trading strategy development are even more cruicial such as: Data Vectorised Backtesting Performance analysis and…

### Welcome to AAA Quants

Welcome to the AAA Quants blog. We are a team of passionate bloggers in the space of machine learning and algorithmic trading. Much of what is happening in this space is still new and experimental and we are dedicated to join the movement and share our ideas. We feel that currently there are three technologies…