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I built a site where nobody can edit their trading track record. Tell me why it won't work.
▲ 6 r/mltraders+2 crossposts

I built a site where nobody can edit their trading track record. Tell me why it won't work.

I got tired of signal sellers with screenshot track records. Winners posted, losers deleted.

So I built the opposite. You post a prediction before the open. It gets hash-locked — no backdating. The market settles it automatically. Everything stays: wins, losses, even the entries that never filled. No accounts. Free. Open source.

Almost called it StockOverflow. Went with QuantRank500 — felt more like a race than homework. Everyone gets the same simulated $500. My own bot is user #1.

Two questions for this crowd: what would it take for you to actually trust a record like this? And what's the first way someone will try to game it?

demo.quantrank500.com if you want to poke it. Code's on GitHub.

u/Purple-Ad6867 — 20 hours ago
▲ 0 r/mltraders

Dudes tell me what im doing wrong cause it looks too good.

Okay so I've been building this system where we train thousands of models from different families (gbm, mlp,etc) and backtest them vigorously and then track their signals to create a leaderboard where the idea is no matter the market condition, there's gonna be a model doing good last couple of hours so we can choose a good performing one for the last X time period. The best models so far have been pretty consistent and consistently outperforming SPY and bitcoin buy and hold. think my Al is lying to me but have told it multiple times in new sessions across coding agents to audit the maths and it says it's good. think the Al are lying to me. Can you help me prove it please?

Edit: detailed methodology at superquacks.com/methodolody

reddit.com
u/dadumdiss — 1 day ago
▲ 0 r/mltraders

What is PortfolioLab?

Hello curious stranger.

With Portfoliolab, you set a goal or throw some tickers at it, it builds a few systematic versions, then they have to get through unseen data and a live paper book before anything real happens. most of them die there, but that's the goal. I got tired of pretty backtests that were just lucky. no broker, no card.

doesn't trade your account and doesn't hold your money. not a calls thing. i mostly just argue about overfitting on here. portfoliolab.ai

u/k1_r1 — 2 days ago
▲ 57 r/mltraders+35 crossposts

ARM +10.3% today — the China exposure math is more interesting than the headline

A lot of the discussion around ARM today centers on its ~18% China revenue exposure (mostly royalty revenue through licensees like Samsung and SK Hynix). Ran the EPS sensitivity instead of just looking at the headline percentage: a 10% cut to that China revenue only moves EPS by about $0.01. The royalty/licensing model has enough operating leverage that revenue shocks don't translate 1:1 into earnings hits.

HPE was up almost identically (+10.0%) the same session, which points more toward broad tech/infra rotation than an ARM-specific catalyst. The AI infrastructure and custom silicon design-win narrative ("physical AI buildout" robotics, edge, data centers) is getting cited as the underlying driver.

Full writeup: https://metricshour.com/briefs/2026-07-10/

Curious if others are seeing the same EPS math or reading the exposure risk differently.

metricshour.com
u/metricshour — 3 days ago
▲ 0 r/mltraders+1 crossposts

Can AI autonomously discover alpha? ~3 months live: +13.7%, 2.32 Sortino

I've been experimenting with an AI-agents generating and testing investment hypotheses and iteratively evolving the resulting trading strategies. I've now been running the discovered trading strategy live from May 15 through Aug 17:

+13.74% return | 1.40 Sharpe | 2.32 Sortino | −14.17% max DD

For comparison, NDX (NASDAQ-100) over the same period:

+1.73% return | 0.25 Sharpe | 0.36 Sortino | −11.24% max DD

Portfolio beta to NDX was 0.92 and correlation 0.59.

The obvious problem: it's only ~66 trading days. For people involved in systematic investing or allocations: at what point would you consider results like these evidence that the underlying discovery process is finding something real rather than getting lucky? I'm exploring whether this could eventually become an investable strategy, and what evidence you'd want to see next.

reddit.com
u/Competitive_Taro_328 — 3 days ago
▲ 15 r/mltraders+5 crossposts

📢 [Community Exclusive] 20% Off Sentimentick Pro for r/topstocksdaily

Hey everyone,

To give back to the r/topstocksdaily community, Sentimentick unlocked a 20% discount on Sentimentick Pro exclusively for our members!

If you’ve been using the basic features to track stock sentiment, upgrading to Pro gives you unlimited access to Sentimentick platform to analyze the market.

How to claim:

  1. Go to https://www.sentimentick.com/app/billing
  2. Use promo code REDDITTOPSTOCKS at checkout.

Drop any questions below or let me know what tickers you're tracking this week! 🚀

sentimentick.com
u/Routine_Bat6675 — 4 days ago
▲ 1 r/mltraders

HESITATION IS DEFEAT

​

After countless backtests and failures caused by the reality of markets and my own curve-fitting, I eventually came up with what I thought was a genuinely good solution.

At that point, I had failed enough times that I already expected this to become another failed project.

But I was wrong.

The first test was excellent. I eventually deployed a real, functional EA from my system and was extremely excitedMaybe too excited that became complacent

I had a goal for how much money I wanted to make over a certain period, and instead of staying aggressive with development, I relaxed.

Then my laptop gave me a scare.

That was when I realized: sooner or later, this thing was probably going to die, and I needed to push while I still had the tools.

So I started pushing harder on my Quant Atlas research system.

Along the way, I realized this might actually be much bigger than I originally thought. I decided to connect everything into one complete pipeline research strategy generation backtesting analysis to improvement.

Then, of course, my laptop died.

Now I have no idea how long I'll have to grind before I can continue properly.

But honestly, the whole experience reinforced something for me hesitation is defeat

Spend less time drawing the perfect system and more time deploying things into reality.

Build it. Test it. Let reality break it. Learn why it broke. Fix it. Deploy again.

Eat the failures.

The market doesn't care how beautiful your theory is. Reality is going to give you the feedback eventually, so you might as well get that feedback early.

Keep pushing.

reddit.com
u/Mother-Duck954 — 3 days ago
▲ 4 r/mltraders+3 crossposts

I fine-tuned a lightweight 3B LLM specifically for verified Python financial calculations (Black-Scholes, Tax Shields, DDB) — Open Source & Local

Hi everyone,

One major pain point when using general LLMs (like GPT-4 or standard Llama) for financial math and quantitative coding is code execution failure or subtle logic/math hallucinations in edge cases.

To address this, I built FinCode-Reasoning-3B, an open-source model fine-tuned on FinCode-Reasoning-v1 (a dataset where 100% of the Python scripts are verified via sandboxed execution & unit tests).

Key Highlights:

  • 100% Verified Execution: Every training example was compiled and unit-tested in a sandboxed Python environment to guarantee syntactically and mathematically correct code output.
  • Lightweight & Private: Built on Qwen2.5-3B-Instruct, meaning it runs blazingly fast locally on standard consumer GPUs/macBooks without leaking sensitive financial data to cloud APIs.
  • Financial Scope: Handles Black-Scholes option pricing, Declining Balance Depreciation (DDB), Interest Tax Shields, WACC, and custom corporate finance calculations.
  • Permissive License: Released under Apache 2.0 (free for personal and commercial research).

Example Code Output (Black-Scholes Call/Put):

Python

import math
from scipy.stats import norm

def black_scholes(S, K, T, r, sigma, option_type="call"):
    d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
    d2 = d1 - sigma * math.sqrt(T)
    if option_type.lower() == "call":
        return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
    elif option_type.lower() == "put":
        return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)

Both the fine-tuned weights and the 100% execution-verified dataset are available on Hugging Face:

  • Model: [https://huggingface.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full]
  • Dataset: [https://huggingface.co/datasets/coslinedev/FinCode-Reasoning-v1]

Would love to hear your feedback or suggestions on additional financial/quant math domains to add to v2!

u/coslinedev — 5 days ago
▲ 7 r/mltraders+1 crossposts

Need help getting my open source HFT project used by more people

Hey guys, I would like to get some feed back on an HFT project I have open sourced. It is not getting a lot of love on github so I thouht I would post about it here.

The system is a for trading futures and simulating HFT strategies on its order book simulator. It has the complete connectivity via MDP3 and iLink3.

I need collaborators to make this system better if anyone is interested. Please drop me a line.

The git hub repo is here: https://github.com/vincent212/kaspar-hft

u/Top-Egg9322 — 6 days ago
▲ 0 r/mltraders+1 crossposts

Backtested my crypto strategy properly, got zero edge. What am I missing?

Built a crypto analysis tool over the last few months (planning and coding alongside Claude, which caught a few of my wrong assumptions along the way). It's not a bot, it reads the market and outputs a plan: entry, stop, take-profits, direction, and reasoning. Manual execution, 10 coins.

The strategy is the standard YouTube-lecture stuff: multi-timeframe support/resistance, confluence zones where levels line up, RSI/ADX/Bollinger, enter at the zones.

I backtested it across 3 years, ~6,000 trades. Then got paranoid the backtest was lying to me, so I went through the code line by line and re-derived individual trades from raw candles, checking for lookahead, making sure fills and exits were honest. It's solid.

Result: zero gross edge. Coin flip before fees, negative after. I also built a base-rate engine (bucket the indicators into market "configurations," trade the ones with a historical lean), same thing. The patterns separate returns in hindsight but carry no info you'd actually have at decision time.

I keep landing on "public indicators don't carry a tradeable edge, everyone sees them at once so it's already priced in." But I don't want to accept that without asking people who've done this.

  1. Is that the expected result, or did I mess up conceptually?
  2. If you found something that worked, was it different data (funding, order flow, on-chain), different timeframes, or execution over signal?
  3. Wanted to test funding/OI/liquidations but Binance only keeps ~30 days. Free source for longer history, or do people just collect forward?

Not selling anything, just trying to work out if I'm chasing something that isn't there. Happy to share methodology.

u/Inside_Fly_8689 — 8 days ago
▲ 4 r/mltraders+2 crossposts

Day 4 results

A few days ago I changed the validation threshold so it scales with the number of times a pair has been searched, instead of using one fixed threshold for every pair.

The change was based on a formula, but I had not checked whether the formula matched an empirical null distribution.

I tested it using SPY 1h’s actual search count: 124 attempts and 20 shuffled null runs.

The measured 95th-percentile null value was 0.9956. The deployed formula predicted 0.8862.

Using the deployed threshold, 13 of 20 shuffled runs exceeded it. The threshold was therefore allowing noise to pass much more often than intended.

The problem was the assumed distribution. The formula treated the metric as unbounded, even though the metric is capped at 1. The curve shape was wrong.

I replaced it with a bounded version, but I’m treating that as a temporary correction. The better approach is probably to measure the null distribution at the actual search scale instead of extrapolating from a small sample.

Twenty shuffled runs are not enough to estimate a precise 95th percentile, so I’m treating this as a diagnostic, not a final statistical result.

I also ran a frozen-model comparison. I stopped one model from retraining or being replaced and compared it with the normal system over the same four-day paper-trading window.

The normal system finished about 1.4 points ahead on the tracked comparison metric. That is a small sample and not evidence of a durable edge.

I’m interested in feedback on:

  • whether 20 shuffled runs is enough for an initial diagnostic
  • whether the null should be generated at the full search scale
  • whether the frozen-model comparison is fair
  • whether the bounded correction is statistically appropriate

The main result is that my original threshold fix was not properly validated. The null test exposed that.

reddit.com
u/dadumdiss — 8 days ago
▲ 3 r/mltraders+3 crossposts

Built an AI/ML-based trading app for my own strategies — looking for Android beta testers

(these are Swing strategy, avg holding period is 30-40days)

Hi Guys

I've been building QuantSync Terminal, an Android app around my own stock trading strategies, and I'm looking for a few people to test it before I release it.

It has:

📈 Live Signals — multiple strategies

⭐ Strategies — performance + historical results

📋 Paper Trade — buy signals and track P&L

📊 Backtest — historical trades and results

Some of my current backtest results, ranked by how often the trades were profitable:

Capitulation Reversal — 75% of trades were profitable | +9.34% average return

ADX Momentum Continuation — 69% | +9.79% average return

RSI Trendline — 64% | +5.78% average return

Trend Continuation — 63% | +6.80% average return

Gap Continuation — 61% | +9.14% average return

These are backtest results from 2022 , where we had slow market still the strategy shown much better response.

I'm mainly looking for people to test the signals, paper trading, charts, filters and overall Android experience and tell me what breaks or what needs improvement.

I'm also planning to give early users 90 days of Premium completely free.

If you'd like to test it, DM me the Gmail address you use with Google Play and I'll send you the testing link.

Please DM the email rather than posting it publicly.

Would love some brutally honest feedback 😄

u/PassageAble — 11 days ago
▲ 2 r/mltraders

My dead end in development

Hi everyone! I've been building my bot for four months now, and during that time I've been exploring the world of HFT trading. But I've hit a dead end at the validation stage (my bot is a scalper). It simply responds 50/50, meaning losses and profits are equal. I'll lose my account due to the exchange's commission. I'd like to ask a couple of questions for experienced HFT traders.

  1. What loss function do you use? (I'm using MSELoss, is that suitable?)
  2. Should I look for patterns not only in the order book and trade history, but also in candlesticks?
  3. What happens if I add EMA to the neural network's responses? Will that help smooth out the neural network's noise?

Thanks in advance for your reply, and sorry for my English; I can't write as well as you. (I used a translator.)

reddit.com
u/Casubed — 9 days ago
▲ 9 r/mltraders+4 crossposts

Portfolio simulator with risk analysis + what-if scenarios — looking for 10 people to test it

Hey everyone — I've been building Strategic Markets, a portfolio simulator, and I'm looking for 10 people willing to actually use it and break it.

What it does:

- Build a simulated portfolio (stocks, ETFs, crypto, bonds, commodities, FX) or recreate your real one

- Risk analysis: concentration, sector/geographic exposure, volatility, Sharpe/Sortino, max drawdown — all computed from your actual holdings, not generic advice

- What-if scenarios: "if I add X shares of this ticker, how does my risk score/diversification/volatility change" — before you actually buy anything

- A small community section (Reddit-style) where you can share your portfolio and get feedback from other users

- An AI assistant for questions about your portfolio

It's live at s-markets.com — free, no card required.

What I actually need from testers:

  1. Add a few positions (real or made up) and try the What-If tool in the Risk tab — does the before/after comparison make sense, or is it confusing?
  2. Sign up flow — anything friction-y?
  3. Anything that broke, felt slow, or just didn't make sense
  4. Post on a couple of communities or create new ones.

I'll reply to every comment and I'm fixing things in real time over the next couple of weeks. Happy to shout out/credit early testers if this goes further.

Not a broker, not financial advice — it's a simulation/decision-support tool, and the community section is peer discussion, not professional advice.

u/Substantial_Net_1677 — 11 days ago
▲ 4 r/mltraders+1 crossposts

What VPS/setup are you using for your trading system?

Curious what people here are actually using for self-hosted algo/trading setups.

A few questions:

  • Which VPS/cloud provider and instance size do you use?
  • What programming language is your system written in? Python, Go, Java, Rust, etc.?
  • Is the VPS only for trade execution, or do you also run analytics, option-chain processing, backtesting, databases, dashboards, scanners, etc.?
  • How much are you paying per month?
  • What are the biggest limitations you face: CPU, RAM, static IP, reliability, deployment, reconnects, monitoring, or maintenance?

I’m asking because I’ve been working on a managed alternative where the user gets a much larger trading environment without having to manage the VPS, networking, static IP, startup/shutdown, monitoring, and other infrastructure pieces themselves.

I think it can provide significantly more compute and functionality at a reasonable monthly cost.

Would be interested to know what people are currently paying and what their ideal setup would look like.

reddit.com
u/avnish-vikas-devops — 12 days ago
▲ 3 r/mltraders

StrategyQuant X (SQX) in Live Trading: Legitimate Tool or Sophisticated Curve-Fitting Generator? (Seeking feedback from experienced quants)

Hello traders!

I'm considering to buy StrategyQuant X but before that I would like to hear from you experiences. I’m looking for honest, long-term live trading feedback. However, I want to filter out the noise. If you treated SQX as a "one-click magic box" and failed, that doesn't tell me much. I want to hear from traders who treated it like a serious quantitative platform, ran strict robustness protocols, deployed portfolios, and traded them live for 1+ years.

​The Context

​There’s a lot of debate around genetic programming software like SQX. Critics call it a "glorified curve-fitting machine," while proponents swear by it as an essential strategy discovery tool.

​To me, determining whether software like this is worth the investment comes down to who is using it and how.

​Scenario A (Not what I'm asking about): A retail trader downloads SQX, clicks "Generate" on default settings without understanding regime shifts or statistical validation, deploys a single strategy OOS, gets blown up live, and calls it a scam. That is user error, not a tool failure.

​Scenario B (What I actually want to know): A disciplined quant designs custom building workflows, enforces strict multi-stage Out-Of-Sample (OOS) data, runs comprehensive robustness checks (Walk-Forward Analysis/Optimization, Monte Carlo parameter/slippage permutations, multi-market validation), constructs an uncorrelated multi-strategy portfolio, and manages risk properly... and still fails in live execution. If Scenario B happens consistently, then the software's core engine or backtest assumptions are flawed.

​Questions for Experienced SQX Users:

​Live vs. Backtest/OOS Correlation: If you passed rigorous Monte Carlo and Walk-Forward tests, how closely did your live equity curve match your OOS/walk-forward expectations over 12–24 months?

​Alpha Decay: How fast do generated strategies decay once deployed live? Do you find yourself constantly cycling out broken strategies, and is the generation pipeline fast enough to stay ahead of decay?

​Execution Reality vs. Engine Mechanics: Did you encounter critical execution mismatches (e.g., order routing, spread expansion, slippage, bar-building logic) between SQX output (MT4/MT5/NinjaTrader) and live broker execution?

​Portfolio Dynamics: Did building multi-strategy, multi-asset portfolios via SQX actually deliver the expected smooth equity curve and drawdown reduction in live market conditions?

​What I’d Love to See in the Comments:

​If you comment, please briefly mention:

​Markets traded (Futures, FX, Crypto, Equities)

​Time live with SQX-generated strategies

​Your general methodology (e.g., types of robustness checks or portfolio construction methods used)

Proof: Screenshot of portfolio/strategy performance etc.

​Appreciate any candid feedback, data, or lessons learned!

reddit.com
u/Electrical_Heron_619 — 11 days ago
▲ 5 r/mltraders+3 crossposts

Do you track “what I thought when I first saw this rock” vs what actually happened?

I screenshot stocks/ETFs when I see them hyped on social media or news etc. along with my thinking at the time. Thinking as in “ oh boy the stock is expensive, let me wait a few days” or “darn! I don’t have the money to buy this now, but let me save for later” etc. weeks later I can’t reconstruct what I was thinking it if I should regret buying - just a pile of screenshots and bad memory.

Thinking about building something that logs ticker + price + reasoning over time as a simple timeline , so you can actually see the pattern.

Is this a problem for anyone else? Or just how I use watchlists provided by these trading platforms? Would you use something like this, or is logging your thoughts each time too much friction?

reddit.com
u/Professional-Pay2827 — 11 days ago
▲ 5 r/mltraders+1 crossposts

My NASDAQ Intraday Algo: 2 Months of Live Demo After a 6-Year Backtest

https://preview.redd.it/jhdtdtbwp2ih1.png?width=1097&format=png&auto=webp&s=dc5dc8f36c73690892ab0ea372bbf9c205257f2e

Hey everyone,

I’m currently running a live demo to validate my backtest results in real-time market conditions, and I wanted to share the progress so far.

I’ve attached the screenshots of my performance. Here is the brief overview:

1. Strategy & Risk Parameters:

  • Position Sizing: Single contract only (No compounding/scaling in).
  • Holding Time: Intraday only. No overnight positions.

2. Historical 6-Year Backtest Performance:

  • Yearly Consistency: Profitable every single year.
  • Annual Return Range: Min 20% to Max 50%.
  • Max Drawdown (MDD): Historically stayed between 8% and 15%.
  • ~1,000 trades over 6 years

3. Current Status & Validation:

  • As for the live demo, I am now 2 months in.
  • Technically, I also started a real live account 1 month ago, but I feel it needs more time to gather enough statistically meaningful data.
  • So for this post, I am sharing the 2-month demo version dataset as it gives a clearer picture for validation.
  • The main goal here isn't short-term profits, but to verify if the live execution actually matches the backtest logic and to check for hidden slippage. So far, the numbers are tracking closely to my expectations, and I will keep monitoring both accounts for a few more months.

Just sharing this as a personal log to track the transition from backtest to live market.

Would love to hear how long you guys typically forward-test your systems before scaling up real money!

u/VOLQUIS — 12 days ago
▲ 2 r/mltraders

How do you decide how much to risk per strategy?

Since a year I’ve been into algo trading and have a collection of strategies, but since trading funded accounts I have to be extremely cautious of my risk. Many people always say to risk a certain fixed % per trade, but it doesn’t make sense to me.

Some strategies have less drawdown than others, some better RF or Sharpe. How do you ‘optimize’ your strategy allocation when trading multiple different EA’s on a single account?

reddit.com
u/br-hm — 12 days ago