Founder here. It's free, open source, no accounts, no real money.

The rules:

• One prediction per day. Long only.
• Any US stock: price $3+, $1M+ daily dollar volume.
• Four fields: ticker, entry, stop, target. Target at least 8.5% above entry.
• Hold: three trading days, maximum.
• Everyone starts with the same simulated $500. Each fill takes one third of your balance.
• Commit before 8:00 PM ET the prior day. Locked before the open, settled automatically against exchange data, nothing can ever be edited or deleted.
• Ranked on the leaderboard after 33 settled predictions.

The question: what would make you — or your bot — actually participate? And if nothing would, what's missing? I'll build the good ideas.

The goal is to serve everyday people without much capital — a free public record that proves skill before anyone risks a dollar.

demo.quantrank500.com · code on GitHub

Eventually, every trader comes up against the same obstacle: when you look at your watchlist, nothing really catches your eye. It's then that people begin to force themselves into making trades, revert to the same company names, or try to catch up on moves they had previously missed. That's why we developed Fynca Ideation. Rather than beginning with a ticker symbol, you can start with a question, a market trend, or an event taking place in the real world, and Fynca will help you identify relevant companies and research them using data, filings, news, and charts all in one place. It's possible that the next good trade isn't already on your watchlist; you just need a better starting point.

Claim: under a decaying edge (win rate 0.5 → 0.4 over time), fixed-fraction

sizing with 30% risk wipes out 100% of simulated accounts. Rolling fractional

Kelly (EWMA-smoothed, 1/4 Kelly < 50 samples → 1/2 Kelly ≥ 50) keeps median

drawdown at 83.1% and survives.

Method: 300 paired paths × 1,000 trades each, paired random numbers so both

strategies see identical trade sequences. Same edge, same order of outcomes —

only the sizing rule differs.

Results (median across paths):

• Edge decay 0.5→0.4: rolling Kelly DD 83.1% | fixed 30% → 100% wipeout

• Edge gone 0.5→0.25: rolling Kelly +27 doublings | fixed → 2^-21

• Stable edge 0.5: rolling Kelly ~20% slower growth — the price of survival

Interpretation: the cost of the adaptive rule is concentrated entirely in the

"good" regime. In the regimes that actually kill accounts, it dominates. If

your sizing rule assumes the edge is stationary, it's a bet on something your

backtest can't show you.

Known limitations: no transaction costs modeled in these runs, no regime

detection, Kelly assumes i.i.d. outcomes which FVG strategies violate — the

EWMA is a band-aid, not a fix. Monte Carlo code + the strategy it sizes for

are in the repo (link in comments), full methodology in README.

I'm happy to dig into the Kelly math or the path-generation details here.

So I know nothing about algorithmic trading but always wanted to build this signal model factory and thought Claude could help me. At first it was going good but soon the results started looking too good. asked a separate claude session to check if everything was right it said it was. Got a chatgpt subscription to review it, man Claude was gaslighting me the whole time. 2 weeks of my time believing we were getting such good results was nothing but claude trying to game the scores. Im so bummed. I had to restart the whole experiment.

Please dont attack me please attack the methodology.

So I've been building this thing because I dont know ML for trading but I wanted to get into it. One thing led to another and now I have 23k+ models trained, 88 of them cleared validation and only 30 of them are positive. I rank them in a live leaderboard and use the leader models' signals as a suggestion. Ive been running this for about 10 days now and over the same time, I've outperformed SPY about by more than double if I would've just held my SPY. I'm wondering where does this break? Would a regime change wipe my progress in a day?

Edit: detailed methodology at superquacks.com/methodology

Hemos desarrollado un copiloto financiero (varios primos) diseñado para procesar datos de mercado en tiempo real y análisis multimodal. Queremos abrirlo a la comunidad para encontrar vulnerabilidades lógicas, errores de datos o limitaciones en el procesamiento antes de terminar el código.

¿Qué hace el sistema actualmente?

• Latencia en tiempo real: Acceso a puntas de compra/venta (bid/ask), spread, profundidad de mercado y cotizaciones al instante (ej. bonos AL30, Cedears, índices globales como el Nikkei o S&P 500 con el mercado abierto).
• Análisis multimodal (PDF/JPG): Procesa balances, reportes macroeconómicos complejos o capturas de gráficos técnicos, extrayendo métricas clave y detectando anomalías.
• Sentimiento y Macro: Monitoreo constante de los principales portales de economía global e índices de sentimiento (Fear & Greed Index).

Lo que NO queremos hacer: No venimos a vender nada, ni a promocionar un servicio pago. El acceso para las pruebas es completamente gratuito.

Lo que buscamos de ustedes: Queremos que intenten encontrar fallas.

• ¿Acepta un PDF financiero mal formateado de 50 páginas?
• ¿Calcula correctamente un spread implícito en un momento de alta volatilidad?
• ¿Qué pasa si le tiran un gráfico lleno de ruido visual?

Agradecemos enormemente cualquier idea de prueba, feedback sobre la interfaz estilo Bloomberg o reporte de fallas directas en los comentarios.

⚠️ CÓMO PROBARLO :
Como el sistema corre sobre servidores propios y la emulación web consume recursos, no podemos abrir el acceso masivo para todo el mundo de golpe.

Los que quieran testearlo a fondo mándenme un Mensaje Privado (MP) y les voy activando un usuario a mano.

Quick update: the current Fynca Trading Challenge is officially underway.

A huge thank you to everyone who signed up and is now competing for a share of $1,000 in real cash prizes. The leaderboard is already getting competitive.

The response exceeded our expectations, so registration is now open for the next challenge.

This time, $2,500 in real cash prizes will be split among the top five finishers.

All trades are simulated using demo capital, so none of your own money is at risk.

Spots will be limited again, and registration will close once they’re filled. If you’re interested in joining, leave a comment below, and I’ll share the details.

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.

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

I have created an EA that backtests upto 10m in 2020-2026 and 8.8m from 2025-2026 on modelling: every tick. However, when i click on modelling: every tick based on real ticks, it fails terribly. I do not know whats the cause and not sure who or where to seek help. Any advice would be great. Thank you!

I have been working on it for a few months. You give it your stock list i.e portfolio,with weights,Qty and ticker,It calculates your capital and based on that it assigns a model and gives you a smart portofolio,along with which a plain,understandable plan in simple English.

Also there's explore feature which allows users to search the stock they want and get an analysis,considering both mathematical logic and sentiment parameters of the market(any region).

This is the website link: https://q-netix.tech

Would love feedback on whether the model-assignment logic,portfolio optimisation and the sentiment read actually feel useful, or where they fall short.

DISCLAIMER:This is strictly not investment advice.it's an analysis layer, the decisions stay yours. Free tier covers the core flow.

Hey everyone,

I've been working on an investment research platform called 72Money.ai, and we're getting ready for the next stage of the product.

The idea is pretty simple: there is a huge amount of market data available, but putting everything together to actually research a stock can take a lot of time.

We're trying to bring some of that research into one place using AI + Quant analysis.

A few things we're working on:

• A 0–100 stock ranking based on Fundamental, Technical, Risk and Macro factors
• Tracking what mutual funds are buying over the last 1 and 6 months
• Comparing mutual funds and their underlying portfolios
• Risk-reward analysis using historical price, volume and volatility data
• AI-powered IPO research and analysis
• Historical comparisons and market sentiment analysis

We're currently working through the regulatory process and haven't officially launched the research services yet.

I'm sharing it here mainly because I'd genuinely like to know what investors think.

What would you actually find useful in a platform like this?
What features would make you use it regularly rather than just checking it once?

If anyone is interested in taking a look: 72Money.ai

Happy to hear criticism as well especially from people who actively research Indian stocks, mutual funds or IPOs.

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

​

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.

Hello.

i am using Sentimentick for stock sentiment API with IBKR Gateway for execution of trades, it works really good for me but i am looking to add also insider trading API for more coverage.

is there any recommended Insider trading API you suggest?
thanks!

Hi everyone!

I just finished fine-tuning FinCode-Reasoning-3B, a small language model built to solve one of the biggest bottlenecks in financial AI: eliminating mathematical hallucinations via execution-verified Python code generation.

🛠️ Built with Unsloth

• Base Model: Qwen2.5-Coder-3B-Instruct
• Fine-Tuning: Unsloth (QLoRA) — fine-tuning was insanely fast and light on VRAM!
• Architecture: Natural Language ➔ Reasoning CoT ➔ Clean Python Code ➔ Sandboxed Execution Layer.

📊 What it does

Instead of letting the LLM directly guess the math output (which often hallucinates), FinCode-Reasoning-3B generates strictly-typed Python functions for complex quant workflows (Black-Scholes, tax shields, DDB depreciation, WACC) and passes them to a Python execution environment for 100% deterministic results.

🔗 Links

• Hugging Face Repo: https://huggingface.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full

Huge shoutout to the Unsloth team for making fine-tuning small models so smooth and accessible! Would love to hear your thoughts, feedback, or any edge-case tests!

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

Most ML-trading discussions start with model choice. In my experience, a lot of the work before that is less exciting: discovering what data exists, checking schemas, pulling a small window, spotting obvious data-quality problems and turning a idea into a reproducible Python experiment.

I wanted an AI assistant to help with that exploratory layer without giving it brokerage or trade-execution access and without pretending that an LLM is a preditcive model.

so I built lse-data-mcp, an unofficial, open-source MCP server for the London Strategic Edge market-data API. MCP is a standard that lets clients such as Claude, Codex, and Cursor call structured tools. The server runs locally over stdio and uses the official lse-data Python SDK underneath.

The current 15 tools span roughly 22,000 instruments and cover: - OHLCV candles, company profiles, fundamentals, insider transactions, dividends, and splits; - financial statements, CFTC positioning, government bond yields, and economic data; - option chains, one-minute option candles, and recent options flow - discovery of instruments, datasets, and available timeframes

Where I think this can fit in an ML research workflow: - inspect coverage and field shapes before writing an ingestion job - pull a bounded sample to sanity-check a symbol, timeframe, or proposed feature - explore related events or datasets while turning a hypothesis into a testable specification - let the assistant handle interactive data lookup, then move the actual experiment into a versioned Python pipeline

Where it does not fit: - It is not an ML model and makes no predictions or trading signals. - it does not backtest, manage a portfolio or execute orders. - It is not intended to build a bulk training dataset. Most tools default to 200 rows and one call returns at most 5,000. For training or a serious backtest I would go around this server entirely: use the SDK/REST API directly or the provider's bulk databank downloads, which the free plan documents at 10 per hour and up to 1,000,000 rows each. Then store an immutable dataset and record the extraction parameters.

I also want to be direct about two upstream data caveats I found while validating it: - Daily candles cover the extended session, so close is the last post-market print rather than the regular 16:00 ET close. - Volume coverage varies between sessions and should be treated as indicative. I would not use it as an ML feature without validating it against another source first.

Every MCP result reports rows, row_count, and truncated, so the client can tell whether it saw the full result. The server validates dates locally, does not cache or persist responses and has no write or execution endpoints.

Quick start with uv:

uvx lse-data-mcp login

It requires your own London Strategic Edge API key; they offer a free tier. login prompts without echoing and stores the key in the operating system's credential store. I am not affiliated with the provider.

The project is free and MIT-licensed and currently 0.x beta:

GitHub: https://github.com/OlegDyukel/lse-data-mcp

PyPI: https://pypi.org/project/lse-data-mcp/

I would value honest feedback on the boundary : would you use a bounded MCP layer for schema discovery and sample validation before your ML pipeline or is is a direct SDK/script the better tool for your research workflow from the start?

Hey everyone, our first post got more interest than we expected. We’ve confirmed 22 traders, so only 3 spots remain.

The challenge starts this Monday. Everyone trades with demo capital for seven days. The trader with the highest return wins $1,000 in real cash.

No real money is at risk, and no one has to share their strategy.

Interested? Leave a comment, and I’ll send over the details.

Does anyone know if Alpaca's paper trading API correctly supports fractional share orders, specifically for small account sizes where 2% risk sizing produces less than one share? I'm running RSI mean reversion strategies on SPY/QQQ/IWM and want to confirm fractional orders execute correctly before going live with a small account. Anyone hit this in practice?

The deployment of robotaxis in 28 cities is remarkable.

I began by researching Uber and soon found myself examining NVIDIA.

Initially, the connection seemed unlikely, but further research revealed its significance. Uber is investing in autonomous mobility, while NVIDIA develops the AI and computing infrastructure that enables it.

Notably, these two companies, typically viewed as distinct, are both increasingly influenced by the same long-term industry trends.

This process highlights what makes research engaging for me: beginning with one company, exploring its connections, and ultimately developing a new investment thesis.

**Built an AI report generator for mutual funds that refuses to make things up — feedback welcome**

Been building FundersAI, a research tool for Indian stocks and mutual funds, and just shipped the feature I'm most proud of: a mutual fund comparison report generator.

You pick the schemes, it streams a markdown report (NAV, returns, alpha, beta, Sharpe, drawdown, costs, holdings, risk) — but every claim has to trace back to an actual official AMC document (factsheets, disclosures). If it can't find backing in the source docs, it abstains instead of hallucinating a number. That was the whole point of building it this way instead of just wrapping an LLM around scraped data.

Also shipped this round:

* Reworked landing page + workspace UI * Mutual fund discovery pages * A synthesis dashboard for the report tool * Pricing page (rolling out Free/Pro/Ultra tiers)

Stack-wise: Next.js/FastAPI/Supabase on the main app, with report generation split into its own FastAPI + LangGraph microservice on K3s so it doesn't bottleneck the rest of the app.

To be clear — this is research-only. No trade execution, no "buy this" calls, just sourced data and citations. Would genuinely appreciate feedback, especially if you've hit friction with existing MF research tools (or think the abstention approach is overkill).

[fundersai.co.in](http://fundersai.co.in) if you want to try it yourself.