I built using AI a local AI financial terminal Bloomberg-style charting + 5-agent consensus engine, zero cloud, free to use [OC]

Hey everyone. I've been building this for a while and finally packaged it up for release. It's called APEX AI Terminal a self-hosted, AI-powered market analysis workstation that runs entirely on your own machine.

No subscriptions. No API costs. No data leaving your computer.

https://i.redd.it/igjl69dp6cwg1.gif

https://preview.redd.it/nnns2k09acwg1.jpg?width=1920&format=pjpg&auto=webp&s=c2088d79e3a30b9023d9fc63bbcb17f7a8de4304

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What it actually does:

The UI is Bloomberg-terminal inspired candlestick charts with 16 configurable overlays (MAs, Bollinger Bands, VWAP, RSI, MACD, ATR, Volume Profile, Fair Value Gaps, Monte Carlo path overlay, Options Max Pain, Market Regime via HMM, and more).

The part I'm most proud of is the **POD engine** five specialist AI agents that run sequentially on any ticker:

- 🔵 Chartist - trend, momentum, S/R, overextension

- 🟢 Fundamentalist - DCF, margins, growth, balance sheet

- 🟡 News Sentry - catalysts, options risk, sentiment alignment

- 🔴 Macro Sentinel - VIX, sector rotation, risk-on/off regime

- ⚪ Orchestrator - synthesises all four into a Confidence Score, Kelly sizing, and a final STRONG BUY → STRONG SELL rating

Other tabs:

- Probability - Monte Carlo (GBM), Bayesian inference, VaR/CVaR, Black-Scholes probability, Scenario analysis, Kelly criterion

- Documents - load any PDF/CSV/TXT earnings report and ask your local AI questions about it

- Screener - natural language stock screener with presets (Value, Growth, Momentum, Deep Value, AI Plays, etc.)

- News- per-headline AI sentiment scoring (−10 to +10) + portfolio-level bulk sentiment synthesis

- AI Trader - autonomous strategy monitoring with live equity dashboard

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The AI stack:

All inference goes through **LM Studio** running locally, you pick your own model. I've been using Mistral-3-14b for the best balance of speed and quality. Larger models (Gemma-4-26b) give noticeably better POD analysis if your hardware supports it. Text-only models work fine for everything except the vision features.

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Why local?

Honestly I got tired of paying for 4 different financial data subscriptions and worrying about what happened to my trade ideas when they hit someone else's server. This runs 100% on-device. Your watchlist, your analysis, your AI nothing phones home.

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Download:

Windows EXE (235 MB zip, no Python needed):
https://github.com/LMLK-seal/APEX-AI-Terminal

The only external dependency you need is LM Studio free, takes about 10 minutes to set up.
You can find all the info you need in my github account.

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Happy to answer questions about the architecture, model choices, or how any of the quant models are implemented. Would love feedback from people who actually trade with tools like this.

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Built with Python · PyQt6 · Matplotlib · yfinance · LM Studio

The sell-side covers Samsara like it's a GPS tracker company competing with Verizon Connect. That framing misses the entire thesis.

What Samsara actually built is a unified sensor platform that ingests data from cameras, temperature monitors, equipment diagnostics, and GPS across millions of physical assets. The switching cost isn't the hardware — it's the years of operational data that customers accumulate and build workflows around. That data gets stickier every month.

The AI angle is real here and it's not a buzzword. When you have continuous video, telemetry, and sensor data across an entire fleet or warehouse operation, the pattern recognition layer writes itself. Predictive maintenance, safety scoring, route optimization — all of it improves with more data volume, and Samsara has more data volume than anyone in the physical operations space.

Net retention rates stay above 115%. Gross margins are expanding toward software-like levels as the hardware becomes a smaller piece of the sale. The customer base is diversifying beyond trucking into manufacturing, energy, construction, and government.

Consensus still has this in the 'fleet management' bucket. The actual TAM is 'connected operations for every physical-world business,' and the penetration rate on that is still single digits.

One big constraint on the AI revolution isn’t algorithms—it’s electricity. Training models and running data centers require massive, continuous power, and national grids are already struggling to keep up with this surge in demand.

This is where SOLV Energy (Ticker: MWH) comes in. The company develops, builds, and maintains large-scale solar and energy storage projects, effectively expanding the supply of clean, reliable power. By accelerating the deployment of utility-scale solar farms and integrating battery storage, SOLV Energy helps stabilize energy delivery and reduces dependence on strained traditional grids. In simple terms, while AI companies consume power, SOLV Energy is part of the ecosystem increasing its production—particularly from scalable, renewable sources that can be deployed faster than conventional infrastructure.

From a market perspective, the story is also gaining traction. Technically, the IPO has broken out to new highs, signaling strong institutional demand and reinforcing the narrative that energy infrastructure—especially tied to AI growth—is becoming a critical investment theme.

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