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Deep Research

Top 25 Algorithmic Trading YouTube Channels (Intermediate/Advanced)

Kaizhi Tang

• 4 min read

Most “algo trading YouTube” content is either (a) platform tutorials with no rigor, or (b) strategy marketing without evidence. The channels below skew toward intermediate/advanced learners who care about process: research hygiene, backtesting methodology, risk controls, and implementation details.

Executive summary

  • You’ll learn fastest by mixing (1) research & backtesting rigor, (2) implementation/coding, and (3) professional risk/portfolio thinking—not by bingeing “strategy ideas.”
  • Treat any channel showing “one strategy to rule them all” as entertainment unless it also teaches validation (out-of-sample, walk-forward, Monte Carlo, sensitivity checks).
  • Use the list as a curriculum: pick one stack (Python/LEAN/MetaTrader/NinjaTrader/Pine) and one “method” channel (testing + robustness), then add domain depth (options, futures, execution).
  • For futures/systematic trading, channels by verified practitioners (e.g., Kevin Davey, Andrea Unger) are disproportionately valuable because they emphasize repeatable process and risk.

How to use this list (a practical curriculum)

  1. Choose your execution stack (one is enough to start):
    • Python (research-first; flexible)
    • QuantConnect/LEAN (research + deploy; institutional-ish workflow)
    • MetaTrader (FX ecosystem; MQL tooling)
    • NinjaTrader (futures tooling; NinjaScript/C#)
    • TradingView Pine (rapid prototyping; good for idea iteration)
  2. Set a minimum validation bar before you believe any backtest:
    • Clearly stated universe + data source
    • No look-ahead / survivorship bias
    • Transaction costs + slippage
    • Out-of-sample + robustness checks (parameter stability, regime sensitivity)
  3. Build a small “portfolio” early (even on paper): 3–5 uncorrelated ideas beat one fragile curve-fit.

The channels (grouped by what they’re best at)

A) Professional futures/systematic process

  1. Kevin Davey (KJ Trading Systems) — systematic futures/FX strategy development, with heavy emphasis on validation and avoiding curve-fit.
  2. Andrea Unger (Unger Academy) — pro-level thinking on strategy simplicity, diversification, and risk controls; good “how professionals think.”
  3. Ali Casey (StatOasis) — robust strategy development mindset; strong on process and “what actually matters” day to day.

B) Backtesting rigor & research hygiene

  1. Serious Backtester — deep dives on backtesting methodology and how to evaluate results without fooling yourself.
  2. AlgoTrade Pro — method-first strategy development; useful if you want to learn how to iterate and validate (often via TradingView/Pine workflows).

C) Python implementation (from idea → backtest → system)

  1. Part Time Larry — practical Python algo building and backtesting frameworks; good for getting an end-to-end workflow.
  2. Algovibes — “myth-busting” backtests and pragmatic Python/data work; helpful for learning how to pressure-test popular ideas.
  3. Coding Jesus — lots of live coding and hands-on strategy work; broad coverage including optimization and ML-adjacent topics.
  4. The Python Quants (Yves Hilpisch) — Python for finance with more math/derivatives depth; great for structured learning.
  5. Sentdex — approachable series on trading bots and research workflows; useful for intermediate coders wanting practical projects.

D) Platform-specific: QuantConnect/LEAN

  1. QuantConnect (Official) — platform/LEAN engine tutorials, webinars, research-to-deploy workflows.
  2. TradeOptionsWithMe — practical algorithm building on QuantConnect, often with Python; good for learning “how to implement on a real platform.”
  3. Quantopian (Archive) — still valuable lecture content on quant strategy concepts and pitfalls; not current, but foundational.

E) Platform-specific: MetaTrader (MQL) and retail automation realities

  1. René Balke (Fx Bot Trading) — MQL5 bot coding + honest automation realities.
  2. Lisa Forex — transparency about what works/doesn’t in automated FX; good for robustness and portfolio thinking.
  3. Trustfultrading — simpler MetaTrader automation examples; useful as a starting point if you’re in MT4/MT5.

F) Platform-specific: NinjaTrader (futures)

  1. NinjaTrader (Official) — NinjaScript/Strategy Builder education; useful if you’re building and deploying on NT.
  2. Vinny E-Mini (Learn Day Trading & ALGOs) — live futures context with “algo tooling” workflows; helpful to see real-time execution constraints.

G) Options + market microstructure / quant finance depth

  1. Option Alpha — options automation concepts (platform-driven); useful for systematic options thinking.
  2. QuantPy — more quantitative finance topics (options, market making, math); good for expanding your theoretical toolkit.
  3. QuantInsti — webinars/lectures with practitioners and educators; broad quant topics and ML-in-finance discussions.
  4. Darwinex (Official) — professional framing around risk, validation, and track-record building.

H) Perspective & interviews

  1. Chat With Traders — interviews with systematic/quant practitioners; great for “how careers and real processes look.”

I) Chinese-language quant channel

  1. 量化投资邢不行 — Mandarin quant tutorials and research-style content; useful if you want a China-market/Chinese-language lens.

J) “Watch the practitioner” (lectures/interviews)

  1. Dr. Ernest P. Chan (talks & lectures across YouTube) — not a single channel, but many excellent talks; best for sharpening mental models (overfitting, regime behavior, execution costs, realistic expectations).

A simple scoring rubric (use this to filter hype)

When evaluating any channel/video, score 0–2 on each dimension:

  • Evidence: do they show methodology, assumptions, and limitations?
  • Validation: out-of-sample, walk-forward, Monte Carlo, sensitivity tests?
  • Implementation: code, data handling, reproducibility?
  • Risk: sizing, drawdowns, portfolio construction, and failure modes?
  • Realism: transaction costs, slippage, liquidity, execution constraints?

A “10/10” video won’t necessarily give you a strategy—but it will give you a process you can reuse.

What to do next (a 2-week plan)

  • Day 1–2: pick a stack; replicate one tutorial end-to-end.
  • Day 3–6: implement one simple strategy + correct costs/slippage.
  • Day 7–10: add robustness checks (parameter sweeps + out-of-sample split).
  • Day 11–14: build a tiny portfolio (2–3 ideas) and write a one-page trading plan (risk limits, deployment checklist).

References