What statistical biases do you need to know to improve your trading strategy performance?

To steer clear of this data mining bias, I learned the importance of following a solid strategy development method. It involved splitting my data into two parts – one for building the strategy (in-sample) and the other for testing it in a completely new scenario (out-of-sample). It's like practicing with some old game footage and then going into a real match to see if your moves still work. Additionally, I embraced techniques like cross-validation and statistical significance tests. It was like adding extra layers of defense against the bias. These methods helped me ensure that my strategies weren't just lucky shots on past data but had a genuine edge that could stand the test of different market conditions.

Data mining bias can make your trading strategies like ill-fitting suits, looking good on historical data but ripping apart in reality. Avoid this by splitting your data, cross-validating, and using statistical tests. Treat your strategies like suits - test them carefully and build a robust wardrobe for confident market moves!

To enhance trading strategy performance, traders must be mindful of biases such as survivorship, selection, data snooping, and look-ahead biases. Careful parameter tuning, avoidance of data mining bias, consideration of changing market conditions, and accounting for transaction costs are crucial. Preventing overfitting and ensuring representative sample sizes are fundamental for drawing reliable conclusions. Rigorous statistical methods and cautious parameter optimization are essential for developing robust trading strategies.

Mitigating Survivor Bias is crucial for any risk modeling assessment. From a bit of research and some basic knowledge gathering, here are a few points to keep in mind; - Include defunct assets: Use data sets that account for all relevant assets, even non-existing ones, if possible. This provides a more realistic picture of performance and potential risks. -Adjust performance metrics: Employ techniques like survivor bias correction factors or selection bias corrections to adjust your performance metrics and account for the missing data. -Perform backtests on simulated portfolios: Simulate portfolios that track defunct assets alongside surviving ones to estimate their hypothetical performance and risk profile.

Recognizing biases like survivorship bias, data snooping bias, and overfitting helps refine strategies. Survivorship bias arises from excluding failed entities, data snooping occurs when multiple models are tested on the same dataset, and overfitting results from excessively tailoring a strategy to historical data.

Furthermore, it must be included some asset that have loss value significantly and ends out of investor mind. There are many assets with a trending period that last as penny stocks.

Adding my two favorite points on the topic; -Blind testing and out-of-sample validation: Blindly test your trading strategies on out-of-sample data (data not used in the strategy development) to evaluate their effectiveness without hindsight bias, which means not letting past wins or losses influence your current decisions. Always choose present information for objective analysis. -Predefined entry and exit rules: We can set up clear rules for entering and exiting trades based on chosen signals, preventing emotional manipulation or post-hoc justification. We must factor in realistic transaction costs and slippage into analysis to reflect real-world execution limitations. There are no shots with crooked outlier data here!

Selection bias occurs when the certain data about trading strategy is excluded or not considered, which can negatively impact your trading, because the results which you're getting are mostly biased so it will not provide you the exact results, So it is advisable to use unbiased data so that it will give you the perfect results.

Confirmation bias is an Achilles' heel for most modelers, mainly because we are tempted to hold a particular bias from our limited understanding of provided data. My tips, to start, include "being your own 10th man with opposing opinion". Here's what I imagine myself doing; 1) Read about outlier data points surrounding the current sample. 2) Try to insert those data points as model training samples in <5% or preferably 2.5%. 3) Try to shake up model performance and compare metrics with a previously robust model that would have otherwise relied on a calm and steady influx of regular data samples.

3 things to consider: 1. what George Soros called 'reflexivity' (other market participants react to your trade executions once you've put a trading strategy into the real world); 2. Sample and population data serve only as imperfect analogs (don't expect historical verisimilitude of outcomes, or linearity in them); 3. The degree of granularity in your data matters (most trading data is of recent vintage, and subject to regular revision; non-trade data suffers this shortcoming with an added bonus of not-being real-time, so expect higher the entropy). The mechanisms by which a trading model addresses these considerations differentiate a good one from something like the now defunct Zillow Offers.