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I tested 4 strategy families out-of-sample, found no edge, and built guardrails for AI trading agents instead (free, open)
▲ 3 r/mltraders+1 crossposts

I tested 4 strategy families out-of-sample, found no edge, and built guardrails for AI trading agents instead (free, open)

Long story short: I wanted an AI agent trading for me. Before risking a dollar
I built an honest backtester (next-bar fills, fees, slippage, walk-forward,
buy-and-hold benchmark).


EMA/RSI, Donchian breakouts, time-series momentum and RSI(2) mean reversion
on BTC and ETH, 1h and 4h, 2022–2026: 
**none beat buy and hold out of sample**
.
In-sample Sharpe of 1–2 collapsed to between −1.5 and 0.4.


What did pass a pre-registered test was boring: volatility-targeted holding
with a trend filter on daily BTC (Sharpe 1.03 vs 0.85 for buy and hold, max
drawdown 42% vs 76%). Not alpha, just less pain.


The more useful output was the guardrail. If an LLM agent is going to trade,
it needs a mandate it can't break and a record nobody can rewrite. So I built
IRL, an MCP gateway: every order is checked against the agent's mandate before
it's sent, the agent's reasoning is sealed first, the fill comes back MATCHED
or DIVERGENT, and the records are anchored on Bitcoin.


Free to try in a minute: `uvx irl-gateway init` (paper trading, no signup
form). Site with a live, editable demo: https://norve.dev. Gateway:
https://github.com/norve-labs/irl-gateway


Research write-ups are in the repo. Happy to be told where my backtests are wrong.
u/GabrielGauss — 7 days ago
▲ 3 r/mltraders+1 crossposts

I need advice/help please

First i would like to say that i am a total beginner and honestly don’t know what i am doing more than half of the time but i would like to share what i am building and i would like advice from the people who know way more than me if this is even worth wasting my time and money(i wasted 4 months and 600 eur already).

I am building a manual trading signal + research system for day trading and swing trading.
It does three main things:
Finds trade setups using predefined rules like ORB/VWAP/RVOL for day trades and trend/momentum/breakouts for swing trades.
Backtests and validates them properly using historical data, walk-forward testing, robustness tests, Monte Carlo, costs/slippage, and strict minimum trade counts.
Protects against fake-good results by fixing survivorship bias, ticker changes, delistings, share classes, historical index membership, corporate actions, and bad provider data before i trust any performance.
Right now, most of the system is built. I am mainly finishing the historical data/identity qualification layer so that when i finally run the strategy backtests, i can trust that the results are based on the securities and data that actually existed at the time.

There is no automatic trading or broker execution. The system is meant to tell you: “this setup qualifies, here is the entry/reference, stop, target/risk context, and confidence/validation state,” while i stay in control.

I am using 3 providers for different jobs:
Massive → mainly for DAY trading intraday data. Think 5-minute bars, ORB, VWAP, RVOL, and possibly reconstructing the RTH liquidity ranking from minute data.
Sharadar → mainly for the historical stock universe and corporate-action context. It gives us the working historical roster, ticker/action history, and helps anchor which securities existed at the time.
Norgate → mainly for stable historical security identity. Its assetid helps us follow the same security through ticker/name changes and distinguish share classes; we’re also checking its historical index-membership data as a cross-check.

reddit.com
u/Sea_Initiative9385 — 7 days ago
▲ 10 r/mltraders+5 crossposts

Backtesting a rule on todays index members leaves out every company that got removed, which is most of what would have hurt you

Something worth checking on any multi year stock test, and I got this wrong in plenty of my own early ones.

You pick a universe, usually the current S&P 500 or the current Nasdaq 100, and you run a rule across ten years of history on those names. The list you used is todays list. Every company that dropped out of the index over those ten years, whether it got bought, went under, or just shrank out of it, is not in the test at all, because it was never in the list you started from.

What you are left with is ten years of history belonging to companies that were healthy enough to still be in the index this morning. The index swaps names out every year and over a decade that is a long list of companies that a current member list simply does not contain.

This hits mean reversion rules hardest. A rule that buys a drawdown and waits gets tested only on the drawdowns that recovered, because the names where the drawdown kept going are precisely the ones that left the index. The test never sees them, so the recovery rate it reports is not the recovery rate you would have lived through.

Its a bit like judging a high school program by asking the seniors how it went. The kids who quit in tenth grade arent in the room to ask.

____

Two things that actually help.

The first is a universe dated to the day the rule fired, so a name enters the test when it entered the index and leaves when it left. Point in time membership data is sold rather than given away, which is why so few retail tests have it.

The second is cheaper. Run the same rule on a fixed basket you chose for a reason that has nothing to do with current membership, a handful of names you picked by sector at the start date, and compare. If the current member version is much stronger, the gap between them is a rough measure of how much of the result was survivorship.

Worth saying that the length of the history is not what fixes this. The equity candles I test on in Agenticks go back to 2000, and a test built on a current name list carries exactly the same bias at 26 years as it does at 10. The bias comes out of the list of names, and it can only be removed there.

u/person-person12 — 11 days ago
▲ 1 r/mltraders+1 crossposts

Seeking Technical Co-Founder for ContraryTrader — AI-Powered Trading Analysis Platform (Equity Partnership)

I’m looking for a technical co-founder / AI software engineer to help accelerate a trading-analysis platform I’ve been developing called ContraryTrader.

A little background: I’m a discretionary trader and technical analyst, not a programmer. Over time I developed a detailed methodology for analyzing markets across Monthly, Weekly, Daily, 4H and 1H charts.

The system goes beyond simple pattern recognition. It evaluates things like:

• long-term trendlines and historical channel geometry

• when logarithmic vs linear chart scale matters

• multi-timeframe support/resistance

• candlestick hierarchy across timeframes

• evolving/nested chart patterns

• confirmation and invalidation levels

• entry location and risk/reward

• sector and macro confirmation

• catalyst/news context

• situations where the correct decision is no trade

I’ve been working with AI to formalize the methodology into a rule-based specification, testing framework, backtesting protocol and early web application.

The goal is to build an AI technical-analysis engine that can take market/chart data and reproduce the type of reasoning an experienced chartist would perform—not simply identify a wedge or generate a generic buy/sell signal.

I’m now looking for someone strong in software/AI who finds this problem genuinely interesting and could potentially help take it to the next level.

Ideal skills would include some combination of:

Python, AI/LLMs, quantitative systems, financial-market data APIs, backtesting, computer vision, backend development, React/TypeScript, cloud deployment, or ML.

You do NOT need to be an expert trader. That is the side of the project I bring.

I’m not currently in a position to pay a salary. I’m interested in finding the right person who would consider joining as a technical co-founder or long-term collaborator for meaningful equity if we determine we work well together.

I’m also not asking anyone to blindly build an idea from scratch. There is already a substantial methodology, documentation, benchmark examples, product architecture, early MVP work and a backtesting framework.

My immediate priorities are:

Automating historical backtesting at scale

Improving automated chart/market-structure recognition

Building the market-data pipeline

Turning the current AI methodology into a reliable production engine

Building a scalable web product around it

If you’re an engineer, CS/AI student, quant-minded developer, or technical founder who is interested in markets and thinks this sounds worth exploring, send me a DM.

I’m especially interested in people who will challenge the architecture and methodology rather than simply code whatever I tell them.

I can show serious candidates what has already been built and explain where I think the real technical moat could be.

reddit.com
u/Old-Reception7469 — 10 days ago
▲ 4 r/mltraders+1 crossposts

Building Sapphire Capitals: Why logic beats hype in trading tech.

Hello everyone,

I’m the founder of Sapphire Capitals, and I’m using this account to pull back the curtain on how we’re building institutional-grade tools for the retail space.

The trading world is currently saturated with "magic indicators" and "get-rich-quick" signals. My goal with Sapphire Capitals is to strip all of that away and focus on what actually moves markets: Mathematical Logic and Objective Data.

What we’re building

The Sapphire Capitals suite isn't a single "holy grail" bot or algo. It’s a specialized ecosystem of apps designed to solve specific data problems:

  • Seasonality: Identifying recurring historical patterns that repeat across decades.
  • Volume Spike: Detecting institutional-grade anomalies before the price catches up.
  • Price Action: Striping away the noise to see the raw structural shifts in the market.
  • Intraday: Precision tools for those operating in the smallest timeframes.

The "Skepticism Killer" Approach

I’m a firm believer that if you can’t show the math, it isn’t worth trading. Every tool we build is designed with a "logic-first" architecture. We focus on:

  1. Removing Emotion: Using hard volume and price data to dictate entries.
  2. Institutional-Grade Logic: Bringing the same frameworks used by floor traders to a clean, accessible SaaS interface.
  3. Transparency: No hype, no "1,000% returns" promises—just the tools you need to find a statistical edge.

Why I'm here

I’ll be posting updates on the development of these apps, technical breakdowns of market movements using our frameworks, and general thoughts on building SaaS in the fintech space.

If you value data over noise, I’d love to connect. I’m always open to feedback from fellow builders and traders who aren't afraid to look at the numbers.

Logic > Hype.

Thank you,

Founder_SapphireCap

reddit.com
u/Founder_SapphireCap — 12 days ago
▲ 13 r/mltraders+1 crossposts

Built an AI research desk for Kalshi that can't place a trade unless I click approve (demo money, 3 days in)

I have been messing with Kalshi's demo exchange the last few days and ended up building a local dashboard for it. runs on my laptop and just talks to the Kalshi API.

It pulls every open market and puts stuff next to it that Kalshi doesn't show, like sportsbook lines, live scores, weather forecasts. then Claude (or Gemini, I can switch) researches a market and proposes a trade with its reasoning. I have to approve every one. there are small hard caps on order size and daily spend, plus a kill switch the AI can't touch.

The part I care most about is that I assume the market price is mostly right. every AI estimate gets blended back toward the market price before anything happens, and the model only gets more weight once its raw guesses actually beat the market over enough settled calls. it hasn't earned anything yet. a second model also gives a quick independent number, and if the two disagree by a large margin, it sizes the bet down.

The parts that broke(and a lot did):

  • ESPN was rejecting every request for a day, so my "live scores and lines" never showed anything and I didn't notice until I checked the logs
  • Kalshi keeps your balance per shard (baseball is on a different one than most things), so my first order failed with "insufficient shard balance"
  • my P&L was ignoring fees, for now

The results I have so far: 2 settled, both wins, which means nothing. the demo books are super thin too. in my paper sim, a strategy that buys when sportsbooks disagree with Kalshi only filled a small fraction of what it wanted, so I don't trust demo results to say much about live.

The ai agent is not going live until it can repeatedly beat the market price in at least one category. If you have built something like this, how do you deal with thin books?

And for anyone running something similar: how much edge do you need before a trade is worth it after fees and spread?

Another question I have is what outside data has been actually useful for you? I'm pulling sportsbook lines and weather forecasts, and wondering what else to add"

u/Difficult_Low_8618 — 12 days ago
▲ 1 r/mltraders

I built an AI-assisted trading analytics platform — curious what the ML crowd thinks

I've been building WorkTree-OS, a trading analytics and risk platform that started because I wanted to understand my own trading data better than a normal journal allowed. unfortunately, I dropped out of college for computer science the Physics and Discrete Mathematics was too much.

The basic idea is:

Import actual trade history → analyze performance → model risk → identify behavioral/performance patterns → give the trader useful feedback from their own data.

The platform currently includes trade analytics, risk analysis, Monte Carlo simulation, prop-firm simulation, consistency analysis, account tracking, and an AI coach called Atlas.

The direction I'm taking Atlas is important: I don't want an LLM pretending it can predict the market or giving generic "AI trading signals."

I want it analyzing the trader.

Eventually Atlas should be able to combine things like:

  • expectancy and profit factor
  • sample size
  • drawdown
  • position sizing
  • time/session performance
  • setup performance
  • Monte Carlo results
  • consistency
  • account/prop-firm constraints
  • behavioral patterns such as overtrading or oversizing

Then explain what the data actually supports and where the trader may be introducing unnecessary risk.

For example, rather than saying "trade smaller," I'd like it to be able to say something closer to:

"Your historical expectancy is positive, but at your current position size the Monte Carlo distribution produces an unacceptable probability of reaching this account's drawdown limit. At your historical performance, reducing risk to X materially improves account survival."

I'm currently building this toward beta and I'd really like feedback from people who work with ML/data rather than just traders looking at a pretty dashboard.

Questions for this community:

Where would you draw the line between useful AI interpretation and statistical overreach?

What models or techniques would you consider for detecting meaningful changes in trader behavior/performance?

And what would you require before allowing an AI system to call a pattern statistically meaningful?

I'm especially interested in ideas around sample-size confidence, regime/change detection, anomaly detection, clustering trading behavior, and avoiding false conclusions from noisy trading data.

Project: WorkTree-OS worktree-os.com

I'm not looking for trading signals or trying to claim AI can predict the market. I'm interested in using the trader's own data to build a better risk and decision-support system.

I'd appreciate people tearing the idea apart technically.

reddit.com
u/Character_Cod_5767 — 11 days ago