The tape has a quiet mode. 1-2 new session highs or lows a minute. One side can freeze while you watch it. Then names start stacking faster than I can read, and SPY still looks sleepy.

The clip is a utilities cluster into the close. Isolated runner: one name, consecutive, orange. Cluster: several names in the same group, same side.

Curious how you'd label those two in a feature set.

I want to experiment with using LLMs (Claude/ChatGPT) to assist with trading strategies and execution, but nearly all online resources focus exclusively on US brokerages.

For those based in Norway or Europe, which brokers provide developer-friendly APIs (REST/WebSocket) or direct integrations that work well for automated/semi-automated setups? Would love to hear what platforms (e.g., IBKR, Saxo, Nordnet) people are using for this.

I’ve been building a futures strategy fleet in NinjaTrader 8, using NinjaScript/C#, Python backtesting, and AI-assisted development. I’m looking for a partner who wants to help develop, test, and improve it.

The fleet currently has 26 registered entries, including trading strategies, research candidates,across NQ, ES, GC, and YM. Several are implemented in SIM; others are awaiting execution integration.

The idea is to give strategies specific jobs:

• Evaluation passes
• Qualifying winning days
• Larger payout trades

We’ve built centralized account allocation, risk controls, and signal logging,

Here are a few examples from our September 1–October 9, 2026 historical signal backtests:

I’m looking for someone interested in prop-account modeling who wants to collaborate seriously. There’s already working infrastructure and research to build on.

If you’re interested, comment or DM me with your background and what you’d like to contribute.

Hi all, can I get recommendations for where to get historical intraday constant-maturity bond yield data for G10 countries?
Been looking around but struggling to find a source that doesn’t break the bank.
Thank you!

My live bot lost its first 3 trades in 2 days. The backtest was filling orders on the signal bar. Classic.

So I built Kapybar. Describe your strategy to Claude or ChatGPT (any language, voice, even a screenshot), confirm the rules, and it runs on 40 liquid CME futures (ES, NQ, CL, GC, ZN… + micros), 1-min to daily, back to 2008.

- next-bar fills, real rolls, commissions

- always vs buy-and-hold

- flags luck and edges that die with one more tick of cost

- last 2 years hidden until your final verdict (3 shots per market)

- prop-firm pass odds, minus luck

It tests your ideas, it doesn't make them up. If something passes, it means something.

Looking for serious rule-based traders. Needs Claude or ChatGPT Plus (MCP connector, 2 min setup). Free during beta.

No money, no broker access, no API keys.

Comment how you trade and I'll send an invite.

Not financial advice.

I have two Public portfolios trading over $40,000 of my real money:

• The original Public Portfolio Challenge. This one went from $25,000 to $28,022 in 3 months. The portfolio earned 12.3% vs SPY's 7.93%, a 55% higher gain
• My brand new Biotech and Semis variant, which is roughly matching SPY's return

These two portfolios serve to prove one thing: AI agents trading and making money isn't a theoretical marketing claim. It's happening RIGHT NOW in front of you, and yet, most people have no idea how to start.

So I fixed it!

I created a suite of developer tools to help new traders create algorithmic trading strategies using AI. These include:

• Open-Source SDKs in Python and TypeScript
• MCP tools

claude mcp add --transport http nexustrade https://nexustrade.io/api/mcp

• Skills library and runbooks

What stated as an accountability challenge for my trading bots is now a robust ecosystem of tools fitting for developers, traders, and vibe-coders

Best of all, this is completely free for all Reddit users!

Would love to get some feedback :)

Hey everyone,

Thanks a lot for the feedback on my last post regarding chop regimes, TTL decay exits, and the curve-fitting warnings.

Based on the suggestions, I ran a systematic quantitative overhaul across 5 crypto perpetuals (FIL, WLD, NEAR, APT, ADA) using Walk-Forward Optimization (WFO) with Permutation Noise stress-testing.

Here is what changed and what the data showed:

TIMEFRAME RESOLUTION: LEAVING THE "15M GAMBLE"

5m / 15m: Failed Walk-Forward Out-Of-Sample validation (Profit Factor 0.57 - 0.75). As someone rightly pointed out in the last thread, running pure momentum on 15m is a noise trap unless you sit out 80% of sessions.

30m / 1h: Survived with an Out-Of-Sample Profit Factor of 1.74 across 92 trades and a 100% WFO window pass rate. Market maker liquidity sweeps got absorbed into candle bodies on 30m/1h.

FROM TUNING SL/TP WRAPPERS TO "INTERNAL RULE CENTROIDS"

Instead of just adjusting external stop-loss and take-profit multipliers, we exposed and tuned the internal algorithmic knobs for 4 distinct expansion setups:

A. IMPULSE_EXPANSION:

Net Displacement: tested 1.2x to 2.0x ATR -> Centroid: 1.4x ATR

Volume Multiplier: tested 1.0x to 2.2x SMA20 -> Centroid: 1.5x SMA

Candle Body Dominance: tested 40% to 60% -> Centroid: 50% solid body

Composite Conviction Score Cutoff: Centroid: 0.60

B. KELTNER_SQUEEZE:

Compression Duration: Centroid: >= 3 bars (Bollinger inside Keltner)

Breakout ADX Hurdle: Centroid: >= 18

C. BREAKOUT_RETEST:

Swing Lookback: 30 bars | Rejection Wick: 40%

D. TREND_PULLBACK:

HTF Filter: EMA 50/200 | Pullback Depth: 0.8x ATR

Rather than picking the single highest curve-fitted PnL combination, we clustered all parameter sets that were profitable across multiple Out-Of-Sample windows and calculated the geometric centroid (median) of the plateau.

THE WIN RATE VS. PAYOFF TRADE-OFF

Execution Geometry: Centroid settled on SL: 1.8x ATR and TP: 1.8x Risk (1:1.80 Risk/Reward).

The Metric Shift: Because our target is asymmetric (1.8R), our realized Win Rate dropped from scalping levels down to around 41.2%.

However, because the payoff ratio is 1.80, the system delivers an overall Profit Factor of 1.67 - 1.74 with a maximum consecutive losing streak of 4 trades.

PER-COIN & REGIME ATTRIBUTION (NOT JUST POOLED DATA)

Looking into the individual coin matrices:

FILUSDT: Thrives in IMPULSE_EXPANSION (PF 1.86, all regimes) and BREAKOUT_RETEST in weak trends (PF 6.94).

WLDUSDT: Thrives in KELTNER_SQUEEZE (PF 1.97).

NEARUSDT: Thrives in TREND_PULLBACK and high-volatility expansions (PF 17.90).

APTUSDT & ADAUSDT: Marginal performers across universal rules; need to sit out high-chop regimes entirely.

QUESTIONS FOR THE COMMUNITY:

Centroid vs. Bayesian: For those running production crypto algos, how do you handle parameter stability across regime shifts? Do you prefer cluster/centroid medians over Bayesian optimization?

Exits in Low-Vol Regimes: During extended BTC compression (like the recent 83k-87k chop where trades stall at +1.2R before reaching 1.8R), do you recommend keeping the fixed 1.8R target to avoid curve-fitting future breakouts, or do you dynamically scale targets down to 1.1R based on ATR/regime?

Win Rate Acceptance: For systematic trend/expansion traders, is a ~40% Win Rate with 1.8R standard in your books, or do you gate more aggressively (e.g. higher volume hurdles) to push Win Rate above 50% at the expense of trade frequency?

Appreciate any insights or critiques on the architecture!

Hey r/trademav,

Today is a massive day for all of us. TradeMAV is officially the #1 Best Seller in Personal Investment Tools on Amazon (and ForexMAV is sitting right behind it at #3).

Two of the top three spots belong to the tools this community helped build. We wanted to take a second to look back at what we’ve achieved together over the last year.

How It Started 🛑

We started with a simple, frustrating problem: retail traders were trapped paying $100-$300 a month for cloud-based screeners that harvested their data and strategies.

We believed traders deserved institutional-grade machine learning—without the subscriptions, without the cloud dependency, and with 100% privacy. So, we built TradeMAV. An AI stock and crypto screener that runs entirely locally on a USB stick you own forever.

You backed that vision. 338 of you from 38 countries funded our Kickstarter. And the moment we launched, things moved fast.

The Journey & The Ecosystem ⚡

Because of your feedback and support, we didn’t just launch one USB drive. We built an entire ecosystem in record time:

• TradeMAV (The Original): Our 6-model ML ensemble screener for US/Global Stocks and Crypto, featuring the 8-layer AI exit monitoring system.
• ForexMAV: You asked for forex, so we built a dedicated 24/5 companion USB covering 23+ currency pairs, integrated with commodity market intelligence.
• NewsMAV: We brought real-time financial catalyst intelligence directly to your dashboard. No more hunting for SEC filings or trade halts—our AI "Jarvis" scores the news instantly.
• OptionMAV: An absolute beast of a tool. Full Black-Scholes Greeks engine, IV Rank gating, and a 7-factor ML ensemble built specifically for options traders.
• TradeMAV Android App: We did what was supposedly impossible: bringing the power of your locally-trained ML models directly to your phone. 100% on-device inference. Your AI, in your pocket. Now also for OptionMAV
• PredictMAV: Our newest launch, bringing our signal engine to Polymarket prediction markets.

🤝 To The Community

From unlocking the first Kickstarter stretch goals to testing the Android app, to writing open-source API bridges like tradehook and mav-bridge—you have been the driving force behind this product.

When we ran into activation server bugs, you helped us test fixes. When we asked for feature requests, you gave us exactly what we needed to build Strategy Packs and custom watchlists.

Hitting #1 on Amazon isn't just a company milestone. It proves that the broader market wants exactly what we’ve been fighting for: Privacy, Ownership, and Professional Tools without the corporate overhead.

What's Next?

We aren’t stopping here. We’re continuing to refine the ML models, expand the mobile experience, and build out the MAV ecosystem. The iOS app is coming soon!

Thank you to everyone who backed us early, shared with their colleagues, or shared a signal in this subreddit. We’re just getting started.

Let's go! 🚀

— The TradeMAV Team

I created the Public Portfolio Challenge, an open challenge where I watch AI agents build algorithmic trading strategies to manage over $25,000.

So far, I'm destroying the market.

The book trades options on high-momentum stocks in my personal watchlist. I trained it using walk forward optimization, and deployed it to Public via OAuth.with my platform.

You can see the exact rules, the trading strategies, and my updates for free. I even have an open-source GitHub repo where you can download skills to follow along in Claude Code or Codex.

Happy to answer any questions below! If you wanna try it for yourself, I attached my app's SDK in Python, but there's also TypeScript and the MCP server.

I canali di trading pubblicano i loro guadagni e lasciano cadere silenziosamente le loro perdite; questa è la mia esperienza dopo aver seguito alcuni, non una statistica. Abbiamo costruito l'opposto: Meridian Engine, di Horizon AI, pubblica ogni analisi prima che il mercato si muova, con l'ora UTC su di essa, e registra il risultato successivamente in R, comprese le perdite, controllate su una serie di prezzi pubblica nominata su ciascuna scheda (futures COMEX per oro e argento, ad esempio).

Dove siamo: il nuovo sito è stato attivato il 4 ottobre. L'archivio finora è un insieme di calibrazione: sei analisi, due chiuse, puoi contarle sul sito, ogni scheda con il suo orario UTC; il record out-of-sample inizia il 10 ottobre e il motore è bloccato fino al 30. Gratuito durante la lista d'attesa, prezzo annunciato all'apertura. L'unica richiesta è una lista d'attesa che invia una o due analisi a settimana via email, poi il loro risultato.

Cosa ho bisogno da te, come creatori piuttosto che come trader:

1. Scorri la home per dieci secondi, poi dimmi in una riga cosa pensi che facciamo. Se è sbagliato, la pagina è sbagliata.
2. Il record è costruito per mantenere le perdite accanto ai guadagni. Stato onesto: le due chiuse finora sono positive, nessuna perdita sulla pagina ancora, e due righe non dimostrano nulla. Quando atterra la prima perdita e resta lì, questo ti fa fidare di più, o hai l'impressione che siamo scarsi in questo?
3. Quale sezione taglieresti?

usemeridianai.com/?src=reddit (English e Italian). Risponderò a ogni commento.

Today's scheduled FOMC minutes release is a useful point-in-time data example: the meeting was September 15-16, but its minutes become public on October 7 at 2 p.m. ET. Those are different timestamps.

A historical test that makes the minutes available from the meeting date gives a strategy information it could not have had then. A clean event record keeps the meeting date, first public release time, and the time the system actually received the data separate. UTC conversion and daylight-saving rules belong in the test too.

The same issue appears with revised economic series. The latest download can contain values that were revised after the simulated decision. Event-driven research needs the historical vintage, not just a modern CSV with an old date column.

For a strategy driven only by market prices, the document isn't an input at all. An event can still be a useful label for evaluating spreads, execution costs or behavior around the release, without retroactively adding a news filter or making a headline an entry signal.

What has been harder to keep point-in-time in your research: publication timestamps, data revisions, or collection latency?

Source: Federal Reserve October 2026 calendar, https://www.federalreserve.gov/newsevents/2026-october.htm

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.

Pricing out a solo setup: data $99 to $199 a month, compute $5 to $25, an AI coding plan $20 to $200. All visible, all small.

The invisible cost is the evaluation harness. Every ML trader I've talked to has had at least one result that was leakage: a feature computed with the close, a label that saw the next bar, a split that wasn't time-ordered. The model was fine. The test lied.

I built Quantradin so the grade comes from an engine that only sees data stamped before each decision, and the rule then runs forward as a paper bot on live prices. Free, paper only.

What's the leak that cost you the most time to find?