Market Pulse - Dual Signal Comparison
10 Stocks: AAPL, MSFT, AMZN, NVDA, TSLA, META, GOOGL, JPM, XOM, SPYData date: . Compute Signal is Return/Vol for the latest 5-minute bar on this day.
Formulas & data source
Compute Signal (per 5โminute bar):
Return = (Close โ Open) / Open
Vol = Std(Return over last 20 bars) + 1eโ4
Signal = Return / Vol
Learning Signal:
Ridge regression trained on the last ~300 Compute Signal records (across the last 7 days). Features include returns, vol, rolling stats, and lagged compute signals (previous 1โ2 bars) so the model can follow the compute trend. The model predicts the current signal; it runs hourly (Lambda) and persists to learning-models/{ticker}/latest.pkl. When there isnโt enough new data, the last saved model is used (LOADED_PREVIOUS).
| Ticker | Compute Signal | Learning Signal | Difference | Rยฒ | MAE | Iterations | Model Status | Converged | Convergence |
|---|---|---|---|---|---|---|---|---|---|
| Loading... | |||||||||
Signal trend (past week)
Track Compute Signal vs Learning Signal over the last 7 days to see how quickly Learning converges to Compute.
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FutureQuant Simulator
FutureQuant Tech Stack
Price Chart
Distribution
FutureQuant Strategy Concept
Distribution-Based Futures Trading Strategy
Instead of predicting exact prices, the FutureQuant strategy forecasts the probability distribution of future price movements, enabling traders to make informed decisions under market uncertainty by understanding both potential gains and risks across different scenarios.
Trading Workflow
1. Get Data
Download market data2. Build Features
Calculate indicators3. Train Models
AI learns patterns4. Start Trading
Paper trade safelyWhat Makes It Smart
AI Learning
- Smart Models: AI that gets better over time
- Multiple Approaches: Combines different prediction methods
- Probability Estimates: Shows confidence in predictions
- Market Patterns: Recognizes 50+ trading signals
Trading Strategy
- Pattern Recognition: Finds when prices are too high/low
- Trend Following: Rides market momentum
- Risk Control: Manages position sizes and losses
- Fast Signals: Updates predictions every second
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Platform Comparison
| Platform | Description | Key Features | Notes | Action |
|---|---|---|---|---|
| OpenRouter | Unified API for commercial LLMs | OpenAI, Anthropic, Cohere, Mistral, etc.; OpenAI-compatible API | Best for commercial LLM aggregation | Learn More |
| Together.ai | Open LLM inference platform | Mixtral, LLaMA, Gemma, etc.; OpenAI-compatible API | Often free/cheaper, focused on open models | Learn More |
| Hugging Face | Model hub & inference endpoints | 100,000+ models, Spaces, datasets, OSS focus | Largest OSS model library | Learn More |
| Fireworks.ai | OSS LLM hosting at scale | Fast, OpenAI-style endpoint, Mistral, LLaMA2 | Production-grade OSS LLMs | Learn More |
| Groq API | Ultra-fast inference (Mixtral) | Low latency, custom hardware, Mixtral | Limited to select models | Learn More |
| Anyscale Endpoints | Hosted open LLMs | OpenAI-compatible, Ray-based, cost-efficient | Performance/cost focus | Learn More |
| Ollama (local) | Run LLMs locally | CLI/API, LLaMA2, Code LLaMA, Mistral | For local/dev use only | Learn More |
| Vercel AI SDK | LLM app toolkit | Unified LLM access, OpenAI, Anthropic, Cohere | Requires Vercel + backend | Learn More |
| LangChain | LLM orchestration framework | Multi-LLM routing, agents, plugins | You write orchestration logic | Learn More |
| Helicone | LLM API logging/monitoring | Proxy, dashboards, analytics | Not a router, but often used with OpenRouter | Learn More |
| PromptLayer | Prompt management & tracking | Logs, versions, routes prompts | Great for prompt versioning | Learn More |
Parameter Explanations
๐ง Summary of Positioning
- Best router for commercial LLMs: OpenRouter
- Best for open-source LLMs: Together.ai, Fireworks.ai
- Best for monitoring/tracking: Helicone, PromptLayer
- Best for orchestration & logic: LangChain
- Best for local dev: Ollama
Use Case โ Best Tool(s) โ Highlights
| Use Case | Best Tool(s) | Highlights |
|---|---|---|
| Commercial LLM Aggregation | ๐ OpenRouter | Unified access to OpenAI, Anthropic, Cohere, etc. |
| Open-Source Model Access | Together.ai, Fireworks.ai | Fast, cost-efficient access to Mixtral, LLaMA, Mistral |
| Prompt Logging & Monitoring | Helicone, PromptLayer | Dashboards, prompt tracking, version control |
| LLM Orchestration Logic | LangChain | Workflow control, multi-model routing, agents |
| Local Model Running | Ollama | Run LLMs (like LLaMA2) locally via CLI/API |
| Fast Open-Source Inference | Groq API | Lightning-fast Mixtral, low latency |
| Custom App Integration | Vercel AI SDK | LLM abstraction with Vercel + custom backend |
| Performance-Focused LLM APIs | Anyscale Endpoints | Optimized OpenAI-compatible APIs, from Ray team |
๐ Detailed Free Model Comparison
| Model Name | Provider | Context Window | Performance | Model Size | Best For | Strengths | Notes |
|---|
Model Performance Comparison
- Best Overall: DeepSeek Chat V3 (chat-optimized)
- Best for Code: Mistral Small 3.2 (strong reasoning)
- Best for Long Context: Kimi K2 (200K tokens)
- Fastest: Gemma 3N (2B parameters)
- Most Experimental: Quasar Alpha (cutting-edge)
- Context Range: 8K - 200K tokens
- Model Sizes: 2B - 24B+ parameters
- All Models: Free tier available
- API Compatibility: OpenAI-compatible
- Total Models: 9 free models
๐ฏ Regime analysis identifies different market conditions and volatility patterns.
๐๏ธ Golf Factor Analysis
AI-powered analysis to determine the optimal time to play golf courses across the United States
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FutureQuant Trader Dashboard
Distributional Futures Trading Platform - Research, Backtesting & Paper Trading
Quick Start Guide:
- Select a Symbol (e.g., ES for S&P 500 futures)
- Choose a Strategy based on your risk tolerance
- View Charts to analyze market data
- Train Models or run backtests to test strategies
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