▲ 0 r/ai_trading
Grid Trading - 0% AI - 100% old school - soon on alphasflowseven.com
Works really well in ranging markets.
u/engineering-AF7 — 4 days ago
u/engineering-AF7
r/AIQuality r/u_engineering-AF7 r/ClaudeAI r/mltraders r/buildinpublic r/ControlProblem r/ai_trading r/reinforcementlearning r/claude
▲ 1 r/ai_trading
Updating prompts in AF7
This is an example of how prompt updating can be done in alphaflowseven.com
You select the model to use for prompt updating - AF7 will report the cost of this action back to you
You write the prompt describing what kind of change you want to make (in this case curing over-trading)
The new proposed prompt is presented alongside the current and you decide what edits to accept and not.
u/engineering-AF7 — 9 days ago
▲ 0 r/ai_trading
Robinhood MCP
Has anyone here built anything with the Robinhood MCP? Experiences and use cases to share?
u/engineering-AF7 — 11 days ago
The anatomy of a buy signal
u/engineering-AF7 — 11 days ago
▲ 1 r/ai_trading
BTC is my nemesis
After having done a lot testing of different configurations of AI councils I have to say that the asset I have seen least success with is BTC. I would think apex status of BTC would make it easier, but not so. SPCX is the one that has worked the best for me, followed by ZEC
u/engineering-AF7 — 16 days ago
Making Claude work real hard
Created a prompt with multiple levels of looping and quality review. I started this 2:30PM Eastern, I'll keep you posted on how long this will run. On Claude Max plan.
u/engineering-AF7 — 19 days ago
▲ 1 r/AIQuality
How do I know if an agent change I made actually made things any better?
This is question I get and that I also ask of myself.
I run into this in AlphaFlowSeven (alphaflowseven.com), a paper-trading platform where a 6-agent LLM council makes trade decisions. Full disclosure: I built it and this is how AF7's reinforcement learning actually works
Prompt and config changes are evaluated by an optimizer with four council slots. One slot always runs the current best config as a control. The other three run variants. All four trade the same market over a fixed 15-day window, and each variant is scored on its excess return versus the control, using all of its closed trades in the window. Raw return isn't used because it mostly measures the market, not the config.
A variant has to beat the control in at least two windows before it replaces it. An excess close to zero is treated as a tie, and ties go to the cheaper config. When a slot frees up, Thompson sampling over each lineage's estimated edge decides what runs next, so a variant with uncertain results gets re-run rather than dropped after one window.
Variants are generated by an LLM that reads the archive of previous configs and their scores, restricted to changing one or two things at a time. Every decision is stored with a fingerprint of the prompt version that produced it, so outcomes can be grouped by version afterwards.
u/engineering-AF7 — 19 days ago
How do I know if an agent change I made actually made things any better?
This is question I get and that I also ask of myself.
I run into this in AlphaFlowSeven (alphaflowseven.com), a paper-trading platform where a 6-agent LLM council makes trade decisions. Full disclosure: I built it and this is how AF7's reinforcement learning actually works
Prompt and config changes are evaluated by an optimizer with four council slots. One slot always runs the current best config as a control. The other three run variants. All four trade the same market over a fixed 15-day window, and each variant is scored on its excess return versus the control, using all of its closed trades in the window. Raw return isn't used because it mostly measures the market, not the config.
A variant has to beat the control in at least two windows before it replaces it. An excess close to zero is treated as a tie, and ties go to the cheaper config. When a slot frees up, Thompson sampling over each lineage's estimated edge decides what runs next, so a variant with uncertain results gets re-run rather than dropped after one window.
Variants are generated by an LLM that reads the archive of previous configs and their scores, restricted to changing one or two things at a time. Every decision is stored with a fingerprint of the prompt version that produced it, so outcomes can be grouped by version afterwards.
u/engineering-AF7 — 20 days ago
▲ 0 r/ai_trading
How do I know if an agent change I made actually made things any better?
This is question I get and that I also ask of myself.
I run into this in AlphaFlowSeven (alphaflowseven.com), a paper-trading platform where a 6-agent LLM council makes trade decisions. Full disclosure: I built it and this is how AF7's reinforcement learning actually works
Prompt and config changes are evaluated by an optimizer with four council slots. One slot always runs the current best config as a control. The other three run variants. All four trade the same market over a fixed 15-day window, and each variant is scored on its excess return versus the control, using all of its closed trades in the window. Raw return isn't used because it mostly measures the market, not the config.
A variant has to beat the control in at least two windows before it replaces it. An excess close to zero is treated as a tie, and ties go to the cheaper config. When a slot frees up, Thompson sampling over each lineage's estimated edge decides what runs next, so a variant with uncertain results gets re-run rather than dropped after one window.
Variants are generated by an LLM that reads the archive of previous configs and their scores, restricted to changing one or two things at a time. Every decision is stored with a fingerprint of the prompt version that produced it, so outcomes can be grouped by version afterwards.
u/engineering-AF7 — 21 days ago
▲ 1 r/ai_trading
Equities added to AlphaFlowSeven
AF7 now monitors select US stocks alongside crypto. v1 covers 8 mega-caps (AAPL, MSFT, GOOGL, AMZN, NVDA, TSLA, META, SPCX) during regular NYSE hours. Live price from IEX, bars from Alpaca SIP. Moving to 24x7 as liquidity permits. alphaflowseven.com
u/engineering-AF7 — 22 days ago
Introducing Claude Opus 5
Introducing Claude Opus 5: a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.
On several coding and knowledge work evaluations, Opus 5 is the new state-of-the-art. It’s also much more efficient than its predecessor—it outperforms other models for a similar or lower cost per task.
According to our automated behavioral audit, Opus 5 is our most aligned model to date. It shows the lowest rates of reckless or deceptive behavior, and the strongest adherence to Claude’s Constitution.
It’s available today on all paid plans and the Claude API, priced the same as Opus 4.8. It’s the default model on Claude Max, and the strongest on Claude Pro.
Opus 5 is also available in Fast mode, which runs around 2.5× the default speed.
Read more: https://www.anthropic.com/news/claude-opus-5
u/ClaudeOfficial — 25 days ago
KimiK3 with ARM levels
Now got a council up and running and creating signals for SOL. I like to provide myself (and users) with as much detail as possible regarding every decision and step the council takes - because that is how you learn and come up with the next tweak to try. That is why we visualize the trades in charts, provide all input prompts and all LLM output on all decisions (including non-decisions).
u/engineering-AF7 — 28 days ago
Inkling for trading?
Thinking Machines released Inkling, open weights and fine tunable. Notably, it was trained for calibration with proper scoring rules and holds its own on ForecastBench.
I wonder if fine tuning it on a large set of trade records would be worth it?
u/engineering-AF7 — 1 month ago
▲ 1 r/ai_trading
Kimi K3
Will be available for all alphaflowseven.com users by Monday’s market opening
u/engineering-AF7 — 1 month ago
LLM for Entry, deterministic exit
The reason for creating alphaflowseven.com was to create a really good test bench for experimenting with AI and trading. We don't do back testing for LLM enabled bots, only paper trading - we feel that is the only way to get real read on the performance.
One lesson learned is that while LLMs in general are pretty good at finding trade entries, they are less good at exits.
Therefore we are now implementing arm and floor levels in addition to stop loss and trailing stops. See below for an illustration of how they work together.
u/engineering-AF7 — 1 month ago
Who knew we would be obsessed with this screen?
What are some things you are doing to control token spend?
On my end, for repetitive complex tasks I am using Fable to come up with the result I want and to create the skill. I then downgrade to a lower model to run the skill.
u/engineering-AF7 — 1 month ago
You create and control your own AI trading team
We built a 6-agent trading council and left the models/temps/prompts open. Here's why.
We assumed there'd be one "best" LLM for this task. There isn't. Claude thinks differently than GPT which thinks differently than Deepseek. Temperature changes how they reason about risk. So we stopped trying to find the optimal config and made it a workbench instead.
Each agent slot takes any model, any temperature, any prompt. You pick a ticker, a timeframe, wire up your six agents however you want, and run the council.
We have been running this for a few months and treating it as a teaching project more than a product. The system forces you to think about why you'd trust each agent's reasoning, which is harder than just tuning a single model.
Come visit, you can see all details of live councils for free, build your own without a subscription.
What configs are you curious about? Or what would break this approach?
u/engineering-AF7 — 1 month ago
example of AI trading council not working at all ...
...we asked Fable to do a deep dive
turns out that trading during US session hours was the key
from now on you can define which sessions you want your AI council to trade in on alphaflowseven.com
trial and error @ work
u/engineering-AF7 — 1 month ago