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!

I've been talking to Gemini about various trading related topics and it got me wondering. Of all the AI agents out there, which one do you feel is smartest, most knowledgeable and most helpful for answering questions pertaining to trading, breaking down strategies and assisting in using trading tools and things of that nature? I've been hearing lots of good things about Claude, but have never used it. So which one do you like better...Gemini? Claude? OpenAI? GROK, etc...?

https://preview.redd.it/ke7yxip8m8uh1.png?width=1200&format=png&auto=webp&s=5d239548ba55d4afe721c5f4fd84d5a326176c2d

The screenshot is a real example.

I asked FX Context to find historical analogs for the latest US PCE release and show how EURUSD reacted after similar events.

It refused.

The reason was explicit:

• only 6 usable PCE calibration events
• 12 are required
• each event needs at least 3 comparable metrics

So instead of fabricating an analog, it returned what it could verify: the latest PCE release, the absence of a valid pre-release consensus, and the actual EURUSD reaction after the release.

That’s the design philosophy behind FX Context.

It’s an MCP server for FX macro context covering:

• US CPI, NFP and PCE
• Fed, ECB, BOE and BOJ statements
• actual vs. consensus and surprise where provenance is valid
• historical EURUSD / GBPUSD / USDJPY reaction windows
• upcoming macro risks
• historical analogs when there is enough calibration data

A lot of the work has gone into making failure states explicit:

• not enough historical samples → return the actual sample count and threshold
• market closed during a reaction window → return MARKET_CLOSED
• no verified pre-release consensus → say so instead of calculating a fake surprise
• incomplete data → preserve provenance and timestamps rather than silently filling gaps

No predictions and no trade signals.

The goal is to keep the deterministic parts inside the data layer:

event → actual → consensus → surprise → market reaction → agent reasoning

and let the model focus on interpretation.

Known gaps: PCE consensus coverage is still incomplete, PCE historical analogs are calibration-gated, and NFP consensus coverage is patchy.

I’m the author. It’s closed source, free to use, and there’s no signup:

https://fx.coco-pier.com/mcp

I’m especially interested in feedback on the tool descriptions, response contract, and failure modes — particularly where an agent should refuse to answer instead of trying to infer around missing data.

Disclosure: I work at Flow AI, the company behind Bobby, an AI trading agent, and RockFlow, the brokerage the account sits at. My own money, small size, under $2K at risk. Not advice, my own calls.

I'm publishing this log from the start. Days 1 to 17 happened between Sept 21 and Oct 7 and come straight from the broker's order export; I'll post them over the next week, then it goes live.

Setup: Bobby is an agent, not a chatbot. It reads the account, does the research, proposes a trade with a reason, and once I confirm it places the order itself through the linked RockFlow account. It also remembers how I like to trade between sessions. The rule for this experiment: every morning I ask it what it would do with the account. It proposes, I confirm or refuse, and nothing goes out except through its chat.

Day 1, Sept 21, 10:59 ET. It proposed one share of AMZN with a limit at $256.05. I said yes. Filled at $255.73, fee $1.40. That's the whole day.

One share is deliberately small. The point for now is whether its reasons hold up over weeks, not the P&L. Two things I'm watching: whether it ever tells me to sell, and whether it changes behaviour after I tell it what I don't want.

One mistake already baked in: I didn't save its reasoning for this trade, and you'll see I didn't for the next few either. From the live part of the log onward every suggestion gets pasted into a file before I act.

Day 1 question for people who've run something like this: how small is too small to learn anything? One share feels like a demo, not a test.

Hi everyone,

We're developing Nexyria, an independent quantitative intelligence project focused on forecasting, market analysis and evidence-based decision systems.

One of our research projects is APEX, an adaptive prediction engine.

The idea behind APEX is relatively simple:

Instead of assuming that one forecasting method works best in every situation, we investigate whether a system can identify the current data environment and select an appropriate method.

We've started testing this approach on business forecasting datasets.

Some early results:

• APEX Business V0.1 improved RMSLE by approximately 1.13% over its baseline in an external Kaggle evaluation.

• APEX Business V0.2 was evaluated on 1,885 daily observations from the Walmart M5 dataset.

• In that second evaluation, APEX matched the strongest baseline but did not outperform it.

• The result was classified as MIXED under our predefined validation protocol.

This second result was particularly interesting.

APEX selected the same forecasting method across all 1,885 observations.

It performed reasonably well, but it did not demonstrate meaningful adaptive behavior.

That raises an important research question:

How can we distinguish genuine adaptation from a selector that simply learns to favor one strong baseline?

We're now considering whether this research approach could eventually be useful in our separate algorithmic trading system, Nexyria Trader.

For example:

Could an adaptive selector help identify different market regimes?

Could it determine when certain trading strategies are more appropriate?

Could it identify environments where the safest decision is not to trade?

We haven't validated APEX for trading, and we're not claiming that it improves trading performance.

Our intention would be to research it offline first, using historical data, walk-forward testing and forward validation, before considering any integration.

I'm interested in hearing from people working on adaptive systems, regime detection or quantitative strategy selection.

What approaches have you found most useful for distinguishing real adaptive improvements from overfitting?

And what validation methods would you consider essential before trusting such a selector in a live trading environment?

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.

I've been using Gemini Pro (Spark) to build out my trading journal by feeding it screenshots, TradingView links, market thoughts, and trade setups. The goal is to build an edge database to refine my system, but the AI is falling short.

My main issue is that it acts like an echo chamber—it constantly agrees with whatever I say instead of challenging bad habits or keeping me accountable. Worse, its visual analysis is inconsistent: it frequently misses key chart mechanics like wick taps, liquidity sweeps, or precise key levels. I spend more time babysitting and correcting its breakdowns than actually learning from them.

I need an analytical AI workflow that acts more like an objective mentor—challenging my logic, catching technical nuances on the chart, and enforcing trade setup rules rather than just validating my bias.

What models, custom setups, or specialized tools are you guys using that actually handle technical chart analysis and strict trade reviews well?

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