QuantLabsNet.com Public Quant Analytics Group

Based on the "Profitable Bots Combined Backtest Report," a clear dichotomy emerges within the trading infrastructure. The 235 bots catalogued are not a homogenous group; they represent two distinct philosophical and technical generations of algorithmic design. This analysis compares Gen1 (the foundational, narrative-driven bots) and Gen2 (the advanced, statistical, and micro-futures focused bots), exploring their performance profiles, risk characteristics, and strategic utility.

1. Defining the Generations

Before comparing metrics, we must define the taxonomy based on the provided data signatures:

• Gen1 (The Macro Bots): These are typically identified by distinct folder structures (e.g., root folders, qln-live-trading, qln-live-trading3) and lack of explicit generational tags. They are characterized by directional bias (Short GC, Long CL), narrative-driven logic ("Safe-Haven," "Iran Tension," "Screwworm"), and often utilize standard contract sizes (GC, CL, BTC). They are the "hunters" of the portfolio—targeting large, thesis-driven moves.

• Gen2 (The Statistical Bots): These bots are explicitly tagged with "Gen2," "Gen-2," "G2," "G2O" (Gen2 Optimized?), or…

I’ve been running a pretty large universe of automated trading bots — over 200 of them are currently flagged as profitable. Originally I wanted to surface only the absolute cream‑of‑the‑crop using a strict filter. When that turned out to be crushing, I relaxed the gate logic to something bare‑bones, and I honestly expected a lot more bots to pass.

The ones that pass all filters is gold, copper, and natural gas. 

Here’s what I was working with.

Strict filter criteria (applied to every bot):

• Minimum trades: ≥ 20

• Win rate: ≥ 50%

I abandoned ship on this HFT thing after spending $123 in a single day — that's the highest I've spent yet, and I got absolutely nowhere with the GUI. I tried using DeepSeek and GLM, thinking I'd save some cash, but no more. It's just not worth it. C++ seems way too expensive to develop with AI assistance. I'll just stick with JavaScript and Python from now on; they're way cheaper.

Disclaimer: This article is for informational and educational purposes only. It does not constitute financial advice, investment recommendation, or an offer to buy or sell any security. All trading involves risk, and past performance — including backtested results — does not guarantee future outcomes. You should consult with a qualified financial professional before making any investment decisions.

June 23, 2026 – The backtest report just landed. Across 33 symbols, 195 strategies generated $3.98 million in combined profit from a $20,195 starting capital pool, with an average Sharpe of 1.07 and an average Sortino of 3.45. But not all profits are created equal – grade, consistency, drawdown, and statistical confidence separate the durable edge from the lucky streak.

Note that the migration to only Rithmic is now done so there is no more IBKR analysis.

Here’s what the data says we should be looking at today, and why.

1. The A‑Graders: Exceptional Risk‑Adjusted Returns