Most "trading bot" courses stop at "here's how to call the Binance API." That's lesson 4 of 30 here. The other 26 cover the things that actually decide whether your bot makes money or blows up: factor research, path-dependent backtesting, position management, error recovery, risk controls, ops monitoring, and scaling.
No fluff, no signal cheatsheets, no "secret indicator" pitch. This is the literal operating manual for the production stack we're running ourselves.
You can read code (any mainstream language) and want to run your own systematic strategy on real capital. You're past "what's a candle" and want to skip directly to building something deployable. You've seen YouTube quant courses that handwave away the actual mechanics — you want the actual mechanics.
You want copy-paste signals to follow. You want a "secret indicator" that prints money. You expect us to share the exact factor weights of Hedonist Intel's live algorithms — those stay proprietary. You won't open a terminal or write code. You expect 8% per day promises.
Most retail bots crash because the infrastructure is fragile. This module is the operations stack: VPS, security, process supervision, exchange API mechanics, websocket reliability, storage. Boring? Yes. The reason 90% of strategies "stop working" is here.
Not "what indicator should I use" — "how do I test whether ANY signal has predictive power, and how do I combine signals without overfitting." This module teaches the methodology. We use a generic short-squeeze detector as the worked example. The exact NEVA factor stack stays in our vault, but the framework you learn here is the same one we used to find it.
Most retail backtests are vectorized — they look at "if I had this signal, what would have happened" using full-window data. They lie. Production backtests are path-dependent: they replay events sequentially, respecting concurrency limits, slippage, fees, and data availability at each moment. The difference between vectorized and path-dependent is often the difference between "+200% backtest" and "−15% live."
Backtests assume orders fill at next-bar open. Live, they fill where the spread allows, with slippage, with partial fills, with API errors, with race conditions, with orphaned stops. This module is the order-management stack and the risk module that prevents catastrophic loss.
A strategy that works for two months means nothing. The hard problem is keeping it working — detecting decay, recalibrating, scaling capital, diversifying across uncorrelated alphas. This module is the operations of a real systematic desk: not romantic, very practical.
Each lesson is a self-contained ~3,000-word read. No video filler, no padding. Production-grade explanations with code, real data, and worked examples from our own logs.
Real production patterns: WS reconnect logic, order placement with idempotency, path-dependent backtester, risk module skeleton, monitoring stack. MIT-licensed for your use.
One payment. Materials updated as exchange APIs evolve, regulations change, our own learnings advance. No subscription, no upsell.
Stuck on a setup question or backtest detail? Email academy@hedonist.trading for the first 90 days post-purchase. Real responses from the people who built the curriculum.
End-to-end worked example: a fully-functional short-squeeze detector with factor research, backtest, execution. Yours to use, modify, deploy. Not the same as Hedonist Intel's NEVA, but built with the same methodology.
If after the course you'd rather subscribe to our live signals than maintain your own algo, course buyers get 4 months at the 1-month rate. Total stack: course + 4mo signals = ~$700 for a working trading operation.
Honest disclosure — these are intentionally NOT taught:
One-time payment in USDT (Binance Smart Chain, BEP-20). Course unlocks within ~2 minutes of on-chain confirmation. Payment instructions appear after you enter your email.
No subscription. 90-day email support included. Lifetime access to all 30 lessons + future updates.
Send $499.00 in USDT on Binance Smart Chain (BEP-20) to:
Send the exact amount. Other tokens or wrong chain = funds lost. Course unlocks within ~2 minutes after on-chain confirmation. Confirmation email goes to the address you submitted.
It's "advanced beginner" friendly. You should be comfortable reading code (Python or JavaScript) and operating a Linux command line. You don't need to know what an order book is — Day 4 covers that. You do need to be able to clone a repo and run npm install. If both phrases are foreign, take an entry-level programming course first, then come back.
The course is designed around running on $300-$1000 of trading capital — small enough to be a tuition expense if it goes wrong, large enough that real-fee economics matter. Day 27 covers scaling beyond that. The lessons match what's practical at small-account scale.
No. Those algorithms are the product we sell as the Hedonist Intel signal subscription. The course teaches the methodology we used to build them — same factor research workflow, same backtest infrastructure, same execution patterns. With this methodology you can build your own variants. We give you the framework; the alpha is yours to discover.
Quant Foundations ($99) is 16 lessons covering market microstructure and crypto-specific concepts. It's the academic primer. Crypto Quant Pro is 30 lessons covering the operational stack — VPS to deployed algo. Different scope, different price. Course buyers who already own Quant Foundations email academy@hedonist.trading for $99 credit toward Pro.
English currently. Russian and Ukrainian translations are planned for Q3 2026 — purchase grants access to all future translations at no additional cost.
If you've opened fewer than 6 lessons and the curriculum isn't what you expected, email within 14 days for a full refund. After lesson 6 is opened, refunds aren't available — at that point you've consumed Module 1 entirely, which is itself worth more than $99 elsewhere. Email support continues regardless.
The infrastructure layer (Modules 1, 4, 5) won't change much — it's about building reliable software, not about specific market behaviors. The strategy and backtest layers (Modules 2, 3) teach methodology, which is durable, not specific factors, which decay. Specific examples we use will be updated as the underlying market shifts. Lifetime access includes those updates.