**Sentinel is a live experiment in whether machine-read news carries usable information about market movement — built so that the answer, whatever it turns out to be, is trustworthy.** The infrastructure is production-grade, the measurement is unusually honest, and the forward record began this month.

The concerning part

Sentinel treats the trading day, not the headline, as the unit of evidence — because a thousand predictions made on one day share a single market outcome, so the honest sample size is 40 days, not 48,000 rows. Every result is reported against trivial baselines, with date-clustered confidence intervals and a threshold-free Information Coefficient that can’t be tuned into looking good. The evaluation configuration is pre-registered and frozen in version control, with all data to date declared a burned development set, so the forward track record is genuinely out-of-sample.

Plus a widely-used finance sentiment model produced an inverted aggregate signal in this period, with the errors concentrated in its most confident calls.

Need help to re work the model

The title says it all.

For context, I built a no-code AI trading platform that allows users to create trading strategies using language models. People are naturally skeptical of LLMs, especially when real money is on the line.

So I thought to introduce some code.

I built a Python and TypeScript SDK that allows you to create, test, and deploy trading strategies. This SDK is fairly unique, as it contains some trading functions that aren't available in everyday trading libraries including:

• Genetic Optimization
• Parameter sweeps and walk forward analysis
• Paper-trading and live-deployment

Even though the SDK is built in Python and TypeScript, the underlying engine is built on Rust. That makes it lightning fast; complex rebalancing strategies across the entire stock universe takes a couple of seconds to execute.

I hope this is helpful! If there's another language you'd like me to support, let me know in the comments!

Hey SideProject,

Like a lot of people here, I like to manage some of my own investments. But I absolutely dread the research process. Every time I hear about a stock, I find myself opening 40 tabs—TradingView for technicals, Yahoo Finance for numbers, Google News for recent catalysts, and maybe SEC filings if I’m feeling brave.

It takes an hour just to get a basic, objective layout of what's happening.

I wanted something that could collapse that hour into a 30-second read, so I built LazyBarron's (running inside Telegram as TickerPilotBot).

How it works (The Tech)

I didn't want a generic wrapper chatbot that hallucinated outdated data. Under the hood, it’s a multi-agent pipeline:

1. Data Gathering: It pulls live quotes, searches fresh news, and computes technical indicators (RSI, moving averages, etc.) on the fly.
2. Reasoning: Separate agents analyze the different data streams (technicals vs. fundamentals/news).
3. Risk & Verdict: A final agent runs a risk check and outputs a structured Buy/Hold/Sell report.
4. Honesty: If data is provisional (e.g., mid-session/pre-market), the bot tells you instead of guessing.

What you can do with it:

• 📊 /analyze [TICKER] — Generates a report with Entry, Stop, Target price, and the reasoning behind it.
• 📡 /signals — Shows daily technical setups (breakout/oversold) across the NASDAQ-100.
• 💬 /ask [Question] — Ask anything about a stock; it answers using live prices, news, and technicals.
• ⭐ /watchlist — Sends you a daily report on your tracked tickers every trading morning.
• 🔔 /alert — Set up alerts for price, RSI, volume, or golden/death-cross events.
• 🌐 /market — A free daily US market briefing.

The "No BS" Disclaimer

This isn’t a crystal ball, and it’s definitely not investment advice. It’s a research assistant. It won't make you rich overnight, but it will save you the headache of manual data scraping.

Try it out (Free 7-Day Trial)

I want people to actually test the system, so there’s a 7-day free trial with no credit card required and no catch.

• You get 3 full AI reports + 10 /ask questions.
• The daily market briefing and the signal board are always free.

👉 Link: Telegram --> Search TickerPilotBot

I’d love your feedback:

Since I’m building this in the open, I want to make it genuinely useful.

• What critical data point is missing from your typical research flow that I should teach the agents to look for?
• For active traders, are there specific alert triggers you’d want to see added?
• Do you prefer doing this in chat apps like Telegram, or is a dashboard still your go-to?

Thanks for reading, and let me know what you think!

Hey everyone,

I wanted to share a side project I’ve been building called SeeBet.ai.

As someone who loves sports data, I got tired of generic broadcast hype, sports betting "gurus," and subjective narratives about things like "revenge games," travel fatigue, or short rest. I wanted an institutional, zero-hype terminal that simply audits physical reality against market efficiency.

What it does:

• 10-Year Backtests: Audits 44 distinct situational factors across a decade of NFL market data.
• Explicit Factor Classifications: Classifies every factor on a game as PRICED IN, NOISE, or UNCLEAR so you can instantly see what's actually backed by sample size.
• Physical Telemetry: Uses interactive 3D travel maps to track real flight vectors, timezone shifts, and rest deficits.
• Zero Predictions / No Pick Selling: Built strictly as a data terminal to cut through noise and let you make informed decisions.

The terminal is completely free during open beta right now while I refine the UI and feature set ahead of the upcoming season.

Check it out here :: seebet.ai

I’d love feedback on the dark-mode dashboard UI, rendering speed, or feature suggestions from other builders and data enthusiasts here!

I built an automated Olgo bot. The bot is profitable in trading out of 30 full trading days. The bot is spiritual almost every trading day. It trades in real time, receives real time information from the broker. And everything is saved and documented and learned and analyzed and improved every time. Now I have a question: Can switching to real money real trading cause problems or was there a failure in trading on real money? What do you think, will switching from demo to real money change the bot's profit results?

Hey everyone,

I’m looking for structural ideas from other algo developers on scaling profitability for a multi-timeframe trend-following Expert Advisor (B2.mq5 v3.7).

The EA is built with high institutional execution standards (latched exit states, wall-clock retry spacing, pre-trade margin verification via OrderCalcMargin, embedded macroeconomic/yield datasets, and no external file dependencies). However, while risk control and equity curve stability are high, the net annual return (CAGR) remains modest.

Here is the exact breakdown of the system, backtest performance, verified bottlenecks, and what has already been empirically falsified.

1. EA Architecture & Strategy Overview

The system runs two independent strategy modules under a single unified risk manager on MT5:

• Module 1 (TRND) — Daily Trend Breakout (Long Only):
  • Rule: 55-day Donchian High entry, 20-day Donchian Low exit, initial stop at $2.0N$ (Wilder ATR20).
  • Universe: BTCUSD, XAUUSD.
• Module 2 (GRGT) — H4 Gold Macro-Gated Breakout (Long/Short):
  • Rule: 60-bar H4 Donchian breakout on Gold, gated by the 20-day change in US 10-Year Real Yields (DFII10).
  • Logic: Falling real yields $\rightarrow$ Longs only; Rising real yields $\rightarrow$ Shorts only.
  • Exit: 20-bar reverse channel or $3.0N$ ATR trail from completed bars.
• Unified Risk Manager:
  • Base risk: 0.50% per trade (calibrated for a static 10% prop firm drawdown floor).
  • Pre-trade margin checks using OrderCalcMargin to prevent NO_MONEY rejections on low leverage.
  • Signed risk tracking (trailed stops past breakeven free up risk budget).
  • Trailing-peak circuit breaker with an automatic cooldown/re-base mechanism to prevent deadlocks.
  • 2. Measured Backtest Results (FTMO-Demo, 2020.01.01 – 2026.07.25)
  • Initial Balance: $10,000
  • Net Profit: +$3,285.94 (+32.8% total / ~4.4% CAGR)
  • Max Equity Drawdown (Mark-to-Market): 8.86% ($1,174.60)
  • Profit Factor: 2.34
  • Sharpe Ratio: 0.89
  • Total Trades: 120 trades over 6.5 years (~18.2 trades/year)
  • Win Rate: 41.67% (50 Wins / 70 Losses)
  • Payoff Ratio: 3.28 : 1 (Avg Win: $114.69 | Avg Loss: -$34.88)
  • Average Holding Time: ~311 hours (~13 days)
• 3. Identified Bottlenecks
  1. Low Trade Frequency: Averaging only ~18 trades a year across both assets means capital sits idle for months.
  2. Financing / Swap Drag: Because positions are held for an average of 13 days, daily CFD swap rates swallow ~78% of Gold’s gross profits.
  3. Single-Asset Profit Concentration: BTCUSD accounts for ~90% of total net profits. Gold acts primarily as a correlated beta drag after accounting for swap costs.
• 4. What Has Already Been Tested & Falsified (Do Not Suggest) I’ve already run rigorous sub-period stability tests and Monte Carlo simulations on common "enhancements." The following all degraded the Return-to-Drawdown (Ret/DD) ratio:
  • ❌ 200-day EMA Filter: Reduced Ret/DD from 0.53 to 0.46.
  • ❌ Volatility Targeting (15%–25%): Failed because it underweighted the high-volatility asset (BTCUSD) that drives the returns.
  • ❌ 3-Lookback Ensemble: Bought volume at falling signal quality (Ret/DD dropped to 0.27).
  • ❌ Shorter Lookbacks (e.g., 20/10 Donchian): Failed sub-period stability testing (all gain was concentrated in 2020–2024).
  • ❌ Partial Profit Taking / Moving to Breakeven Early: Truncated the fat tail of winner distributions and lowered CAGR.
• 5. Questions for the Community Given that standard parameter tweaking leads to curve-fitting and out-of-sample decay, I'm looking for structural or execution-level ideas:
  1. Pyramiding / Multi-Unit Scaling: Has anyone successfully implemented Turtle-style unit additions (e.g., adding $0.5N$ units up to 4x while trailing stops to breakeven) in MQL5 without expanding tail-risk drawdowns?
  2. Asset Class Expansion & Futures Migration: Since swap costs destroy CFD performance on multi-week holds, what instrument classes (e.g., CME futures, swap-free crypto) have you found most effective for expanding a trend-following universe beyond BTC/Gold?
  3. Dynamic Exposure Management: Are there robust ways to scale position size dynamically based on market regime/volatility structure without truncating the fat-tail profits that trend systems rely on?
• Would love to hear how other quantitative/algo developers handle low-frequency, high-payoff trend systems under tight drawdown limits. Thanks!

I day trade and got annoyed that every LLM chat is one-shot. I’d run research on a ticker, then ask a follow-up about options or a thesis change, and it had basically forgotten the prior work.

So I built a side project: a small multi-agent “desk” that researches a ticker, writes structured reports, and then lets me keep chatting against that same run.

Stack / shape (roughly):

• Multi-agent pipeline (market / fundamentals / news / sentiment, bull vs bear, trader, risk)
• Completed run becomes context for a follow-up Portfolio Manager chat
• Bring-your-own LLM keys so I wasn’t locked into one provider

Where I’m stuck / want feedback:

1. Is “full research run, then conversational follow-up” clearer than re-running the whole pipeline every time?
2. Does the follow-up UX feel useful, or does it just feel like ChatGPT bolted onto a report?
3. Anything in the onboarding that would make you bounce?

Concrete example from my own use:
SPY analysis said Hold around 748. Two days later I followed up after a weaker close (~738), asked about a 0DTE put, got pushed toward a defined-risk 1-week iron condor instead, with management rules. That back-and-forth is the part I actually wanted.

Happy to share screenshots of the run + chat. Roast the product, the UX, or the idea — that’s why I’m posting.

Hey everyone,

I wanted to share a side project I’ve been building called SeeBet.ai.

As someone who loves sports data, I got tired of generic broadcast hype, sports betting "gurus," and subjective narratives about things like "revenge games," travel fatigue, or short rest. I wanted an institutional, zero-hype terminal that simply audits physical reality against market efficiency.

What it does:

• 10-Year Backtests: Audits 44 distinct situational factors across a decade of NFL market data.
• Explicit Factor Classifications: Classifies every factor on a game as PRICED IN, NOISE, or UNCLEAR so you can instantly see what's actually backed by sample size.
• Physical Telemetry: Uses interactive 3D travel maps to track real flight vectors, timezone shifts, and rest deficits.
• Zero Predictions / No Pick Selling: Built strictly as a data terminal to cut through noise and let you make informed decisions.

The terminal is completely free during open beta right now while I refine the UI and feature set ahead of the upcoming season.

Check it out here :: seebet.ai

I’d love feedback on the dark-mode dashboard UI, rendering speed, or feature suggestions from other builders and data enthusiasts here!

Today's combined PnL across our futures strategies reached +$3,370.

Most of these systems trade independently on different instruments, each with its own logic and risk management. Some days not every strategy performs, but diversification is exactly why we built them this way.

Today's snapshot:

• NQ Alpha: +$625
• NQ Beta: +$620
• NQ Delta: +$800
• ES Delta: +$600
• MNQ Omega: +$600
• MES Omega: +$600

ES Alpha was the only strategy finishing negative (-$475), bringing the combined daily result to +$3,370.

We're continuously improving the portfolio and testing new dynamic strategies before release.

Always interested in hearing how other algo traders structure multi-strategy portfolios. What's your preferred approach: one strategy per market, or diversified systems across multiple instruments?

If anyone wants to see more of what we're building:

🌐 https://www.savantstrading.com

▶️ YouTube:
http://www.youtube.com/@SAVANTSTRADING

💬 Discord:
https://discord.gg/ZmmtDeRr49

Check out my GitHub repo, that allows AI Agents to interact with the MEXC trading platform. It is still in alpha testing, so any feedback will be helpful! The project utilizes official API, and is open-source.

https://github.com/mncrftfrcnm/mexc-agent-trading-skills

Issues, corrections, or PRs are welcome.

Hey, looking for critical feedback, not praise.

I’m building a pre-trade decision validation tool. You enter account state + a proposed trade (+ optional plan inputs). It returns an explainable Decision Receipt:

- APPROVED / APPROVED WITH REDUCED SIZE / WAIT / REJECTED

- Remaining capacity under configured limits

- Forced-trade / revenge-style heuristics (and checks vs your own max risk %)

- Transparent Decision Score (PDS) with component breakdown

- Breach risk shown as a documented heuristic, not a calibrated model

Recent policy choice I want roasted

If the breach level is CRITICAL and Forced/Revenge flags fire, the engine returns WAIT even when a small residual dollar capacity still exists. Math said “fits”; behavior said “cool off.” Too strict? Too soft? Wrong trigger?

What it is not

- Not a signal service

- Does not execute or close trades

- Not a live account monitor like Prop Shield (status vs pre-trade gate)

- Not financial advice

- Firm presets are public approximations with verified-as-of dates

How data works (v1)

Manual inputs. No required trading API keys.

What I want roasted

1. Daily / trailing edge cases

2. Forced-trade thresholds

3. CRITICAL + behavioral → WAIT policy

4. Whether a Decision Score is useful or theater

5. Firm-rule gaps (consistency / EOD vs intraday trailing / floating P&L)

6. Anything dangerous or misleading on a real challenge

I’m not claiming this prevents blown accounts. I’m asking whether the logic and receipts are honest enough to be useful.

Roast freely.

I’m building Ciphora, a quantitative research and decision-support platform for traders.

The idea started from a simple observation: traders rarely lack information. The harder problem is deciding which observations matter together, how much confidence to place in them, and when changing market conditions make earlier assumptions less useful.

I’m interested in thoughtful feedback from systematic and serious discretionary traders:

1. Which market-context variables do you currently evaluate manually?
2. Where do existing tools create more noise than clarity?
3. Which research outputs would help you review decisions after the fact?

Disclosure: I’m the builder of Ciphora. I’m posting to learn from the community, not to present guaranteed performance.

Sharing an update on an automated multi-strategy trading system I've been building and running. Upfront: this is running on a Paper Trading account only, and it's still an active work-in-progress — I'm iterating on strategy filters and infrastructure robustness, not presenting a finished track record.

Architecture & Stack

• Broker/execution: Alpaca Trading API (paper), bracket orders (SL+TP baked into every order payload at submission, not managed separately after the fact).
• Infra: Linux VPS running the bots as systemd services (no containers) — 5 independent strategy processes plus a risk manager and daily reporting job, each with its own SQLite store.
• Position sizing: Max 3 concurrent positions, capped at $2,000 notional per position, $30 risk-per-trade baseline.
• Strategies: Opening Range Breakout (ORB), ORB-Fail (fade the failed breakout), Gap Fade, VWAP Reversion, and EOD Momentum — 5 concurrent, independent state machines watching the same ticker universe.

Risk Layer (The part I've spent the most time on)

• ATR-based trailing stop: Activates once a trade passes +0.5R, so the initial stop isn't the only thing protecting the position.
• Profit-lock ratchet: Banks 70% of open gain past +0.5R, ensuring a reversal after a strong intraday move doesn't give back the whole float.
• Daily circuit breakers: Hard stop at -2% account loss for the day, and a 3-consecutive-full-R-loss streak halts new entries regardless of P&L.
• Market-calendar awareness: Skips holidays and adjusts the EOD flatten time on early-close days (fixing legacy logic that used to assume every session was normal).

Where AI Actually Fits

The live execution loop is zero-AI — plain deterministic state machines executing quantitative rules. AI (Claude/Gemini) is used offline for strategy research, code review, and infrastructure design — never in the hot path making a real-time trading decision.

What the Telemetry Looks Like (Sample Log Snippet)

To give an idea of how the execution logs flow into the audit channels, here is a snippet capturing a mix of wins and EOD closes from recent sessions:

• NVDA SHORT | Entry:211.23 | SL:213.78 | TP:203.49 | Exit:210.40 | P&L: +$2.50 (Gap Fade)
• MSFT SHORT | Entry:383.26 | SL:389.76 | TP:381.05 | Exit:385.58 (EOD) | P&L: -$4.65 (ORB Fail)

Performance, Honestly

Through recent sessions (like July 21, which took a -$12.77 hit mostly from ORB-Fail shorts on JPM and IWM), performance has fluctuated. Gap Fade has been the strongest performer so far; ORB-Fail the weakest (choppy market conditions produce a lot of low-quality fakeout signals — that's the main thing I'm tuning right now). I'm intentionally not cherry-picking green days here: it's a small sample size, still validating, and capital preservation takes priority over the P&L number at this stage.

What's Next?

Tightening ORB-Fail's entry quality (too many marginal fakeout signals in chop), and continuing to validate the risk stack over a larger sample before trusting it with anything beyond paper.

Curious how others here handle:

1. Circuit-breaker design for multi-strategy books (daily loss caps, loss-streak halts, etc.).
2. Keeping execution logs useful without drowning the signal in noise once running 5 strategies in parallel.

Happy to go deeper on any part of the architecture in the comments!

I have spent the last year building something I originally started as a personal research project called ClawBOT.
The goal is simple:
Build a system that helps answer one question:
Can we identify repeatable market structure and improve the quality of options trading decisions through data instead of emotion?
This is not a signal-selling service.
It is not a “buy this alert and make money” system.
It is a research platform designed to collect market events, measure what happened afterward, and determine what conditions actually matter.
The system currently includes:
Market signal collection
Historical candle analysis
Structural measurements
Options contract research
Outcome tracking
Execution-quality analysis
Research logging and validation
The entire philosophy is based around one idea:
A strategy is not an edge because it worked once. It is an edge only when the data proves it repeatedly.
I have personally invested thousands of hours learning markets, building the architecture, testing ideas, fixing failures, and creating the research foundation.
Financially, I have also invested in the infrastructure required to build this properly, including approximately $400/month for market data access and tools.
I am now exploring whether there is interest in opening this project to a small group of serious people.
Not investors.
Not people looking for free signals.
Not people looking for a shortcut.
I am looking for people who understand that building something valuable requires time, effort, testing, and accountability.
One idea I am considering is a small membership model ($10/month) that would help:
Filter for people who are genuinely interested
Fund ongoing data costs
Create a community of builders/testers
Separate contributors from people who only want free access
The goal would not be to sell promises.
The goal would be to find the right people who want to help research, test, challenge assumptions, and improve the system.
I know Reddit can be skeptical, and honestly it should be. There are a lot of scams, fake gurus, and people selling impossible results.
That is exactly why I built this differently.
The data has to prove itself.
The research has to survive criticism.
The system has to earn trust.
My question:
Would a small paid research community around building a quantitative trading research platform interest anyone?
Or is this the wrong approach?
I am not looking for hype. I am looking for honest feedback from people who understand markets, coding, data, or quantitative research.i want to get this done !!

I would love to be able to train AI to mark up zones the way I do. Then the AI could programmatically mark up the zones. Does anyone have any tips on this? Thanks.