Market Pulse - Dual Signal Comparison
10 Stocks: AAPL, MSFT, AMZN, NVDA, TSLA, META, GOOGL, JPM, XOM, SPY

Data date: . Compute Signal is Return/Vol for the latest 5-minute bar on this day.

Formulas & data source

Compute Signal (per 5‑minute bar):

Return = (Close − Open) / Open Vol = Std(Return over last 20 bars) + 1e−4 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 1–2 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 isn’t enough new data, the last saved model is used (LOADED_PREVIOUS).

Ticker Compute Signal Learning Signal Difference R² MAE Iterations Model Status Converged Convergence
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Signal trend (past week)

Track Compute Signal vs Learning Signal over the last 7 days to see how quickly Learning converges to Compute.

⚠️ Alert Log

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🤖 AI Platform Comparables
What is this? This section compares leading AI/LLM platforms and toolkits that serve as alternatives or complements to OpenRouter for LLM access, orchestration, monitoring, and local inference.
Commercial Open-Source Monitoring Orchestration Local
(Interactive chart coming soon: popularity, speed, or cost comparison)

Platform Comparison

Platform Description Key Features Notes Action
OpenRouterUnified API for commercial LLMsOpenAI, Anthropic, Cohere, Mistral, etc.; OpenAI-compatible APIBest for commercial LLM aggregation Learn More
Together.aiOpen LLM inference platformMixtral, LLaMA, Gemma, etc.; OpenAI-compatible APIOften free/cheaper, focused on open models Learn More
Hugging FaceModel hub & inference endpoints100,000+ models, Spaces, datasets, OSS focusLargest OSS model library Learn More
Fireworks.aiOSS LLM hosting at scaleFast, OpenAI-style endpoint, Mistral, LLaMA2Production-grade OSS LLMs Learn More
Groq APIUltra-fast inference (Mixtral)Low latency, custom hardware, MixtralLimited to select models Learn More
Anyscale EndpointsHosted open LLMsOpenAI-compatible, Ray-based, cost-efficientPerformance/cost focus Learn More
Ollama (local)Run LLMs locallyCLI/API, LLaMA2, Code LLaMA, MistralFor local/dev use only Learn More
Vercel AI SDKLLM app toolkitUnified LLM access, OpenAI, Anthropic, CohereRequires Vercel + backend Learn More
LangChainLLM orchestration frameworkMulti-LLM routing, agents, pluginsYou write orchestration logic Learn More
HeliconeLLM API logging/monitoringProxy, dashboards, analyticsNot a router, but often used with OpenRouter Learn More
PromptLayerPrompt management & trackingLogs, versions, routes promptsGreat for prompt versioning Learn More

Parameter Explanations

Model: Choose the AI model that best fits your needs. Each model has different strengths and capabilities.
Temperature: Controls randomness in responses. Lower values (0.1-0.3) make responses more focused and deterministic. Higher values (0.7-1.0) make responses more creative and varied. Range: 0.0 to 2.0.
Max Tokens: Limits the length of the AI response. Higher values allow longer, more detailed responses. Lower values create shorter, more concise answers. Range: 100 to 4000 tokens.

🧠 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 → Best Tool(s) → Highlights

Use Case Best Tool(s) Highlights
Commercial LLM Aggregation🏆 OpenRouterUnified access to OpenAI, Anthropic, Cohere, etc.
Open-Source Model AccessTogether.ai, Fireworks.aiFast, cost-efficient access to Mixtral, LLaMA, Mistral
Prompt Logging & MonitoringHelicone, PromptLayerDashboards, prompt tracking, version control
LLM Orchestration LogicLangChainWorkflow control, multi-model routing, agents
Local Model RunningOllamaRun LLMs (like LLaMA2) locally via CLI/API
Fast Open-Source InferenceGroq APILightning-fast Mixtral, low latency
Custom App IntegrationVercel AI SDKLLM abstraction with Vercel + custom backend
Performance-Focused LLM APIsAnyscale EndpointsOptimized OpenAI-compatible APIs, from Ray team
Want a visual diagram of these tools by category? Let us know!

🔍 Detailed Free Model Comparison

Updated Models: This section provides a comprehensive comparison of the latest free models available on OpenRouter, including context windows, performance metrics, and use case recommendations.
High Context Fast Inference Multilingual Code Focused Chat Optimized Experimental
Model Name Provider Context Window Performance Model Size Best For Strengths Notes

Model Performance Comparison

Context window size vs. model performance rating
🏆 Top Recommendations
  • 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)
📊 Performance Metrics
  • Context Range: 8K - 200K tokens
  • Model Sizes: 2B - 24B+ parameters
  • All Models: Free tier available
  • API Compatibility: OpenAI-compatible
  • Total Models: 9 free models
Rolling Metrics Benchmark

Compare Naive OOP vs Rolling Deque vs SoA on synthetic tick stream (VWAP, vol, rolling max/min).

How the 3 implementations are evaluated

Concepts used to evaluate each implementation:

  • ✅ Streaming computation — Each tick is processed once as it arrives; no full replay. All three impls consume the same stream via on_tick(ts, price, size).
  • ✅ Sliding window — Metrics are over the last W seconds only. Naive keeps a list and evicts by time; Deque and SoA use time-bounded deques so the window slides in O(1) per eviction.
  • ✅ Amortized complexity — Naive: O(W) per tick (rescan). Deque & SoA: O(1) amortized (incremental update + monotonic deque for max/min).
  • ✅ Cache-friendly design — Naive: list of objects (more indirection). SoA: deques of primitives (ts, price, size separate), better locality and fewer allocations.
  • ✅ Incremental state — Deque and SoA maintain running sums (e.g. sum(price×size), sum(size)) and rolling variance state; on eviction they subtract. Naive recomputes from the window each time.

The benchmark measures throughput (ticks/sec), latency (ms/tick), and peak memory under the same stream and window; correctness is checked by comparing VWAP, vol, and rolling max/min across all three.

Polygon = last 5 days minute bars (bar-as-tick)

🏌️ Golf Factor Analysis

AI-powered analysis to determine the optimal time to play golf courses across the United States

Filter by Location

Select a location to see the top golf course recommendations for that area.

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