Built an RSI extreme reversal algo for MNQ/NQ — 4.5 years, 7,157 trades, here's everything
Strategy
4.5 years confirmed. 7,157 trades. No cherry-picking.
Win rate: 92.0% Profit factor: 2.35 Total P&L (1x): +$133K EV per trade: $7.29 Losing months: 1 out of 53
Been building a pure RSI extreme reversal strategy on MNQ/NQ futures. Finally have enough data to share something real.
The core idea: don't trade every overbought/oversold condition. Wait for an extreme — only the most stretched readings within a rolling window qualify. No extreme, no trade. One filter that eliminates most noise.
YEAR-OVER-YEAR CONSISTENCY:
No year is negative. Profit factor has been expanding since 2023.
STREAK ANALYSIS — 7,157 trades:
Max winning streak: 71 consecutive wins Top streaks: 71, 67, 67, 52, 46 Average streak: 12.7 wins Streaks of 50+: 4 times
Max losing streak: 3 consecutive losses — happened only 3 times ever 4 in a row: never
MONTE CARLO — $6K → $1M with 6 MNQ (5,000 simulations):
Hit $1M: 93.9% of simulations Bust rate: 6.1% Median time to $1M: 21 months Fast track (10th percentile): 17 months
LIVE PERFORMANCE — Memorial Weekend 2026:
24 trades. 23 wins. 1 loss. Win rate: 95.83% Gross P&L: +$315.00 | Net after fees: +$287.96 Best trade: +$47.00 | Worst trade: -$2.50 Avg hold time: 2min 10sec | Longest trade: 11min 13sec
The one loss lasted 11 minutes — the longest trade of the session. The time stop did its job. The actual losing trade was only $2.50. Fees represented 91.54% of total gross losses that session.
HOW THE EXITS WORK:
WHAT THE DATA TAUGHT ME:
No input settings. No curve fitting. Parameters are fixed and have never changed. Locked to MNQ and NQ only.
Recently developed a strategy and did I complete grid search on variables to optimize the strategy which helped a lot rather than manual trial and error process.
Any views on the process and how to make it better?
So i am a beginner in algotrading and i need some help with what to do next. i have seen this whole video Algorithmic Trading Python for Beginners - FULL TUTORIAL by quant program
and i understood compelty everything it shows. ive created some basic strategys reusing these concepts. Today i saw this video Market Profile and Support/Resistance Levels With Python by neurotrader and i was so lost. I didnt understand a single thing. Can someone maybe help by telling me what to do next so in the future i can maybe understand the conext of this and other future videos liek that.
With how powerful LLMs and AI agents have become in 2026, creating trading strategies has never been easier. You can prompt Claude or spin up a custom agent and get a fully coded, backtested strategy in minutes — often with impressive-looking Sharpe ratios and equity curves.
The challenge isn’t how do I generate ideas? anymore. It’s which ones are actually worth risking capital on? Been thinking of adding a formal Verification Phase after strategy generation, sth that goes beyond traditional backtesting or walk-forward analysis. The idea is to systematically stress-test a strategy across multiple independent dimensions before it ever touches live capital:
The goal isn’t to “guarantee” performance, but to force the strategy to survive adversarial scrutiny and surface failure modes early. Already published a few papers on quantitative risk methodology and verification techniques that support building this kind of independent layer. But I’m curious what the community thinks:
Would love to hear thoughts
Here's yet another EOD strategy I've been playing around with lately. It is akin to momentum ensemble and aggregates the scores of a few fixed momentum kernels. It is more or less parameter-less (the only parm is the exposure level, which is heavily quantized). Uses the same S&P500 basket as my other backtests. Like always, executions are MOC, nothing exotic.
The equity curve is a 26-year GA optimization backtest (CAGR/maxDD = 20%/20%) and the CAGR/MaxDD histograms are from 5000 26yr MC sims of the winning chromosome. Open to comments and constructive criticism.
Is algo trading really profitable?
I’m learning Python and trading, and I want to know the real truth about algo trading from experienced people.
Is it profitable long term?
How much time did it take you to become profitable?
I know there have been some workarounds over the years, but everything I can find these days seems to be non-working
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Building a systematic strategy around option selling on Indian indices (BankNifty/Nifty). The core logic is Greeks-based — it reacts to how options behave relative to spot moves, so backtesting on end-of-day or even 1-minute OHLC data is basically useless for this. The signals depend on tick-level data of both the index and the option chain simultaneously, which means I can only generate meaningful signal history by running the engine live (paper or real) and logging everything myself.
I've been doing this for a while and have a growing dataset, but I'm genuinely unsure about the threshold where I should feel confident enough to deploy real capital.
A few things I'm wondering:
- How many trades / weeks / months of forward data do you consider the minimum before trusting a strategy?
- Does expiry-day behavior need to be separately validated? (Gamma dynamics feel very different from regular days)
- How do you account for the fact that your early logs might have bugs that skewed results, even if you've since fixed them?
- Is there a statistical framework you use — Sharpe threshold, min sample size for signal reliability, etc. — or is it more gut feel after seeing consistent behavior?
Not looking to get rich overnight, just want to be methodical about this. Would love to hear from people who've actually gone through this process with systematic/algo strategies rather than discretionary ones.
Any hard-learned lessons appreciated.
Would I get accurate results backtesting with AI if I gave it candle data and a detailed strategy description? Would this also work for detecting events and distributions around it?
Hi everyone,
I’ve hit a wall with TradingView. I’ve been using Pine Script to run custom signal logic across multiple watchlists, but their recent decision to cap lists at 500 symbols has effectively broken my workflow. I need to scan at least 2,000+ symbols simultaneously, and TV’s cloud limitations are no longer cutting it.
I’m looking for a platform that offers a TradingView-like experience (clean charts, easy scripting, alerts) but with much more horsepower under the hood for large-scale scanning.
My specific needs:
Heavy Custom Logic: I’m not looking for a basic price/volume screener. I need to run complex, multi-condition scripts (like I did with Pine) that process the entire universe on the fly.
Scale: Must handle 2,000+ tickers without lagging or hitting arbitrary "symbol caps."
Alerting/Dashboard: I need to be notified or see a real-time list when my script triggers a signal on any of those 2,000+ stocks.
What I'm considering:
QuantConnect: I know it's the "gold standard" for algo trading, but how is the UI/UX for someone used to TV’s visual environment? Is the Python/Lean learning curve worth it just for scanning?
TrendSpider: I’ve heard their "Market Scanner" is powerful, but can it handle the same level of script complexity as a dedicated coding environment?
Local/Python Frameworks: I’m open to running things locally if there’s a framework that handles the data pipe and provides a decent UI for visualization.
Has anyone else here "graduated" from Pine Script to something more robust for market-wide scanning? I love the ease of use of TV, but I can’t deal with the constant nerfing of capacity. What’s the best "middle ground" between a retail charting app and a full-blown institutional HFT setup?
Thanks for any suggestions!
I'm an 18M with a few months of free time before starting college. how and where should I learn Python? I'm not really sure which specific quant role I want to go for, but I've heard Python is pretty important. I know the basics of the language... just looking for a bit of guidance.
cheers!!
edit: as people are downvoting this I would like to clarify something. I am a full time trader, have been trading for 16 years and currently profitably run 7 algorithmic strategies. i am a big supporter of backtesting and this is post is not saying that backtesting is bad. It’s just explaining why blindly backtesting thousands of strategies will cost you money.
I recently saw multiple people on Reddit following this approach of automatically testing thousands or even tens of thousands of strategies to then afterwards live trade whatever survives the backtesting and demo trading so I just want to create this short post and explain why this won’t work and why it will cost you a lot of money.
Basically the idea when creating a strategy and when then testing this strategy on out of sample data during a backtest as well as during demo trading is that afterwards you can ask yourself “what’s the probability that a non-profitable strategy would perform this well?”. Then usually if you only did this with one strategy the answer would be that it’s highly unlikely that an unprofitable strategy would perform well on out of sample data as well as demo trading and you could confidently conclude that your strategy does indeed have an edge and that it will most likely continue working when live trading.
However when backtesting for example 10.000 strategies that changes the whole question. in that case if one of your strategies performs well the relevant question wouldn’t be what’s the chance that this specific strategy performed so well just by chance but instead “what’s the probability that at least 1 of 10.000 strategies would perform that well just by pure chance?”. And the answer is basically always that it’s very very high and that most likely your strategy won’t continue working when live trading.
There are also ways to simulate this exact probability which I can share in case this intuition isn’t enough but I just felt like sharing this because this approach seems to become more and more common and it will cost people a lot of money.
When I see the rate of failure in the algo trading community I wonder how many people are actual traders themselves? I see people complaining about not being able to build a single bot that can be profitable for more than "just a year or two" when there is simply no real trader in the world who trades the exact same way all the time! They all adapt their strategy to the market.
To "make it" in algo trading I believe you have to be a trader first with robust knowledge of the market you trade, and a developer/programmer second. Unfortunately I see too many people who are inexperienced with the markets who think their background in programming is enough to help them build profitable trading bots.
All profitable algo traders I know are seasoned manual traders first who transition to algo trading and then they usually only use scripts/algos to help them execute in a semi-automated fashion. They all like to keep some level of discretion, monitoring market sentiment, bond yields, news of the day i.e. stuff that's not so simple to code into a bot, then when it's time to execute they let the machine do its thing.
Algo trading isn't a magic wand that lets you bypass the hard work of acquiring real screen time and market knowledge. That has to be your foundation. It's like a weekend golfer buying a set of Tour-level clubs hoping it will magically fix his terrible swing. The clubs might be state-of-the-art, but if you don't understand the fundamental mechanics of the game you're still going to slice the ball into the woods on every drive!
It looks like EODdata dot com is down right now, Friday morning, May 8, 2026, at 8:50 AM. Is he aware? Anybody know anything?
Know of any alternatives?