Data quantitative techniques are very important in modern trading as they help traders use the data they have at hand together with mathematical techniques to capitalize on various trading opportunities while managing their risk. For a novice, developing a quantitative model may appear like a huge task, but it can be broken down into various steps which makes the task easy. This guide will help you to build your first quantitative model in a step by step approach.
Step 1: Start by Debriefing What You Want
Is this what you want?: “In my case where I am using historical and moving average to predict stock price movements, yes I want my model to predict accurately”
Is this model scope and application accurate? : “Yes, keeping the key focus on one asset class or a single indicator for the first model development is advisable.”
Whether it’s predicting stock price, detecting trends or better still optimizing a portfolio, you need to know the wide range of functionalities that your model offers.
With the risks applied, you can now deep dive into the data and computations needed to bring your model to reality.
Step 2: Sources, Collection and Extractions of Data Set
Because data patterns unfit for use may exist, this practice will : Delete unfit data values as well as clean the extracted data containing price and volume of your selected asset.
Stick to reliable platforms like Yahoo finance, Bloomberg, or Quandl for your data extraction.
To gain precious insights into your model parameters, structure and ideal variables which will garner authority, construct a historical data base on the asset at hand.
Ensure the variables are standardized to offer consistency across points covering and controlling data sets like volume over a price amalgamation.
Time-series analysis: Make sure the timestamps are recorded in the correct format for analysis.
For instance, If the aim is to evaluate the stock market then make sure to include adjusted closing prices in data such that it will cover the adjustments needed due to splits and dividends given for the shares.
Step 3: EDA (Exploratory Data Analysis)
EDA is important as it portrays the characteristics and patterns embedded in one’s data. The following aspects can be considered:
Graphical Representation: Try using line graphs for trend observations and histograms for distribution analysis.
Statistical Summaries: Generate averages and observe standard deviations and correlations.
Anomaly Detection: Determine and address outliers that are likely to interfere with your results.
For example the annual return on a stock may show trend-following systems with the stock price together with a moving average over 20 days periods.
Step 4: Modeling strategy
The first class of models is quantitatively based and fall within statistical or machine learning techniques. For your first model, begin with simpler approaches:
Rule Base Models
Take for example a moving average crossover where signals to buy/sell are provided through two moving averages (short – long) crosses.
Regression Models
Use regression to quantify the degree of association of stock return and the movement of the market.
Mean Reversion Models
Search for assets that will tend back to their historical residual mean price after some time.
Step 5: Build the Model
Essential Elements
Inputs: State the types of data your model will cater for ie historical prices, indicators.
Logic/Rules: Prepare specific and straight forward rules based on your selected strategy.
Outputs: Specify the results that will occur such as price predictions, buying or selling signals.
Example: Moving Average Crossover
Determine both short-term (10 day) and long-term (50 day) moving averages.
When the short term average crosses above the long term average, issue a buy signal.
Today forget the long term average and create a sell signal if the short term average crosses below it.
Step 6: Backtest the Model
Why Backtest?
Backtesting makes it possible to approximate how your model could have performed given a sequential set of historical data.
Steps in Backtesting
Make two sections of the data, training and testing, to simplify the process.
Practice on the training set which will assist in further development, while the testing set is used for performance assessment.
Using your models generated signals, create mock trades.
Quantify figures of metrics related to return, risk, and accuracy.
Step 7: Optimize the Model
Parameter Tuning
Investigate all settings, as moving average periods may yield benefits to more parameters of your model.
Using grid testing for all your parameter combinations will aid in more systematic exploratory research.
Avoid Overfitting
Overfitting happens when a model is so intricate that it does well with old data, but fails miserably on new data. This can be managed through:
Simplicity in the model.
Implementing techniques of cross-validation.
Step 8: Evaluate Model Performance
Use the following conditions to judge the efficacy of the model:
Returns: The final amount of loss or profit the pre determined model earns.
Sharpe Ratio: This type of ratio determines the level of return earned in excess of the risk-free rate per unit of total risk.
Maximum Drawdown: The greatest loss from the highest peak in the maximum drawdown.
Win Rate: Proportion of all transactions which brought income.
Step 9: Run on Live Environment
In case your model did not disappoint you during back testing feel free to rollout the model in a real trading environment. Always begin with baby steps:
Engage in paper trading to execute live trades without any financial consequences.
Assess the performance of the model and alter the model as per results in live settings.
Step 10: Continue Repeating Until Improvement
Quantitative models are not something you can “set and forget.” Markets change and your strategies and plans should be altered complimentarily. Keep checking and modifying your model to:
Introduce new sources of data.
Consider all elements of changing market conditions.
Don’t be shy to experiment with machine learning or alternative data.
Conclusion
In reality, building your first quantitative model is an adventure into the unknown. All that is required is a concise and simple idea accompanied with consideration and proper metrics design. Over time and practice you will be able to formulate models and strategies that are increasingly sophisticated, and allow you to take greater risks in quantitative trading.
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