Blog posts about algorithmic trading#
This section contains various blog posts and articles related to algorithmic trading, quantitative finance, and systematic strategies.
Note
For academic research papers, see the Papers section. For AI and machine learning topics, see the AI section.
How to avoid overfitting trading strategies#
Running a lossy trading strategy would be a very costly mistake, so we spend a lot of effort on assessing the expected performance of our strategies. This task gets harder when we have limited data for this evaluation or when we experiment with the strategy for a longer time and risk manually overfitting the strategy on the same out-of-sample data.
A Trend Following Framework for Gold, Bitcoin, and Nasdaq#
A practical framework for systematic trend following across Gold, Nasdaq, and Bitcoin futures. The approach combines a Donchian Channel for trend detection with a Chandelier Stop for risk management. Key components include: volatility-targeted position sizing (risking 2% of equity per trade with stops at 3x ATR), a two-stage exit mechanism using both protective trailing stops and technical exits based on lowest low breakdowns.
The system targets “fat tails” while keeping maximum drawdown to -15%. With a 47% win rate but favorable risk/reward (average win 4.30% vs average loss 2.06%), the framework emphasizes that systematic execution beats emotional guessing. Assets are chosen for their high volatility, strong trends, and low correlation.
By Petr Podhajsky. Tools used: RealTest & Norgate Data.
Why Mean-Variance Optimization Breaks Down#
Mean-Variance Optimization (MVO) is a central framework for portfolio construction, yet practitioners quickly encounter a paradox: the mathematically “optimal” portfolio built from estimated inputs is often unstable, highly leveraged, and disappoints out-of-sample. This is not a minor implementation detail—it is a structural consequence of combining a high-dimensional optimizer with noisy estimates of expected returns and covariances.
This article develops MVO from first principles and explains, in a mathematically explicit way, why raw MVO tends to maximize estimation error. It surveys the spectrum of practical fixes organized around two levers: improving or regularizing the inputs (expected returns and covariances), and constraining or regularizing the optimizer (the feasible set and objective). The unifying theme is that almost every successful fix works by injecting bias in exchange for a large reduction in variance of the resulting portfolio weights.
By VertoxQuant.
Short Scamtrash / Long Distinguished Crypto#
A practical investigation into the “quality factor” for cryptocurrency perpetuals trading. The core strategy is simple: short scammy, low-quality crypto perps while going long large-cap crypto against it. This works better in crypto than traditional equities because markets are less efficient and there are abundant opportunities to identify low-quality assets.
The post demonstrates how to build a simple “trashmetric” derived from trading volume to sort perpetuals into buckets by quality. Backtests show that trashier assets have worse average returns while more distinguished assets perform better. The strategy trades once a week, going long the least trashy third of the universe and short the most trashy third, with volatility-targeted position sizing. The author discusses ways to improve the strategy by adding more proxies for trashiness (instability, size, liquidity) and being smarter about portfolio turnover and trading costs.
By Robot James.
Simple Crypto Momentum Strategy: Buying Corrections#
A straightforward crypto trading approach shared by Pavel demonstrating that simple strategies can work effectively in cryptocurrency markets. The strategy involves: buying corrections on the highest momentum coins, holding positions for one day, and applying a basic regime filter to avoid unfavorable market conditions.
The equity curve shown suggests consistent performance with this systematic approach. The strategy exemplifies how basic momentum and mean-reversion concepts can be combined—buying dips in strong trending assets while using regime filtering to stay out during adverse market conditions.
By Pavel (Robuxio).
Forecasting Market Regimes with the sUSDe Term Structure#
An exploration of how the sUSDe term structure on Pendle can serve as a forward-looking signal for crypto market sentiment and regime forecasting. The article explains how crypto prices are heavily influenced by leveraged trading in perpetual futures, and how Ethena’s sUSDe effectively captures the basis from funding rates through delta-neutral strategies.
With multiple sUSDe expirations trading on Pendle, the market reveals implied yields across various maturities, producing an onchain term structure. This yield curve indicates whether markets expect rising funding rates (contango) or declining rates (backwardation). The article demonstrates that the term spread—the difference between back month and front month implied yields—is highly correlated with underlying yield regimes and produces a stronger signal for returns than the underlying yield alone. Historical analysis supports using this term structure as a leading indicator for changes in cost of carry and BTC price levels.
By Luke Leasure.
Using Log Returns and Volatility to Normalize Strike Distances#
A foundational tutorial on why log returns matter in financial and derivatives modeling. The article starts from first principles, explaining how the constant e represents continuous compounding and how the natural logarithm measures the time needed to reach a certain level of growth. This mathematical foundation is then applied to understanding why upside and downside price moves are not symmetric in compounded return space.
The practical application focuses on normalizing option strike distances. For a $100 stock, $150 and $50 are not equidistant in the world of compounding—$150 is closer. The article shows how to compute equivalent distances using log returns and extends this to normalizing for volatility, allowing traders to compare strikes across different assets with different volatility profiles. This framework is essential for proper options analysis and understanding moneyness in a mathematically consistent way.
By Kris Abdelmessih.