Recent Quant Links from Quantocracy as of 08/31/2025

Albert Einstein had a way of capturing deep truths in simple words. His quote is a reminder, especially relevant to us when building models. Stripping away unnecessary complexity is vital, but going too far risks oversimplification: a model that looks neat but fails to capture reality. This week, we will implement the idea from the paper Volume Shocks and Overnight Returns, by lvaro Cartea,

Often we have a trading system with a countless number of trades (in my case 70,000,000) with little to no way to understand actually what is going on. Sure, we get massive printouts and tear sheets with a ton of figures that quantify our strategy. But, what about on a trade-by-trade basis? What we really need is to understand the quality of our trading systems on a trade-by-trade basis. Its

Code changes. Data changes. Outputs change. Somewhere between the first analysis and an odd position in production, little mismatches creep in: a misstated value, off-by-one date ranges, rounding shifts, subtle drift in calculations, missing IDs. The most reliable way to catch them is to compare a new DataFrame to a previously validated onea reconciliation, or rec, test. recx is a lightweight

Central banks regularly adjust the economys monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing.

How do we find edges? First, we must be clear about what constitutes a good idea. It isnt as simple as it having to make money. The risk profile must also be tolerable. This is a personal preference. Next, we need to be able to trade it. Robinhood wont let you sell naked options. You cant trade the Indian markets or crypto derivatives from the US. Retail cant trade OTC. These