mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-24 22:15:18 +00:00
fix(neural-trader): Add missing technical indicators to live mode
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
rUv
parent
e7b36d1ac2
commit
ee8c199fe5
12 changed files with 705 additions and 60 deletions
|
|
@ -1,11 +1,11 @@
|
|||
{
|
||||
"actorSpecification": 1,
|
||||
"name": "ai-synthetic-data-generator",
|
||||
"title": "AI Synthetic Data Generator - Enterprise Mock Data with MCP & Actor Integration",
|
||||
"description": "Generate unlimited, high-quality synthetic data with TRM/SONA self-learning. NEW: MCP server for AI agent integration + one-click data from Google Maps, Instagram, TikTok, Amazon scrapers. 6 use-case templates (Lead Intelligence, Competitor Monitor, Support Knowledge). Simulate Bloomberg terminals, medical records, supply chains, e-commerce, and more.",
|
||||
"seoTitle": "AI Synthetic Data Generator - MCP Server & Actor Integration for RAG",
|
||||
"seoDescription": "Generate synthetic data with MCP server support. One-click integration with Google Maps, Instagram, TikTok, Amazon scrapers. 6 pre-built templates for Lead Intelligence, Competitor Monitor, Support RAG. TRM/SONA self-learning.",
|
||||
"version": "2.2",
|
||||
"title": "AI Synthetic Data Generator - ONNX Embeddings, MCP & 21 Actor Integrations",
|
||||
"description": "Generate unlimited synthetic data with ONNX-powered semantic embeddings (all-MiniLM-L6-v2, bge-small). 21 actor integrations (Reddit, Yelp, TripAdvisor, Zillow, Booking.com + more). 12 use-case templates. TRM/SONA self-learning. MCP server support.",
|
||||
"seoTitle": "AI Synthetic Data Generator - ONNX Embeddings & 21 Actor Integrations",
|
||||
"seoDescription": "Generate synthetic data with ONNX semantic embeddings. 21 actor integrations (Google Maps, Reddit, Yelp, Zillow). 12 templates. TRM/SONA self-learning.",
|
||||
"version": "2.5",
|
||||
"buildTag": "latest",
|
||||
"minMemoryMbytes": 256,
|
||||
"maxMemoryMbytes": 4096,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
{
|
||||
"title": "AI Synthetic Data Generator",
|
||||
"description": "Generate unlimited, high-quality synthetic data for testing scrapers, training AI models, and building agentic applications. 18 data types including e-commerce, Bloomberg terminal, medical records, supply chain, and more. Now with one-click Apify actor integration and use case templates. Powered by TRM/SONA self-learning.",
|
||||
"title": "AI Synthetic Data Generator v2.5",
|
||||
"description": "Generate unlimited synthetic data with ONNX-powered semantic embeddings (all-MiniLM-L6-v2, bge-small). 21 actor integrations (Reddit, Yelp, TripAdvisor, Zillow, Booking.com + more). 12 use-case templates. TRM/SONA self-learning. MCP server support.",
|
||||
"type": "object",
|
||||
"schemaVersion": 1,
|
||||
"properties": {
|
||||
|
|
@ -58,7 +58,7 @@
|
|||
"integrateActorId": {
|
||||
"title": "Apify Actor to Integrate",
|
||||
"type": "string",
|
||||
"description": "Actor ID to pull data from (e.g., apify/google-maps-scraper). Used in 'integrate' mode.",
|
||||
"description": "Actor ID to pull data from. 21 actors supported. Used in 'integrate' mode.",
|
||||
"editor": "select",
|
||||
"enum": [
|
||||
"apify/google-maps-scraper",
|
||||
|
|
@ -73,7 +73,15 @@
|
|||
"apify/website-content-crawler",
|
||||
"apify/cheerio-scraper",
|
||||
"apify/news-scraper",
|
||||
"apify/linkedin-scraper"
|
||||
"apify/linkedin-scraper",
|
||||
"trudax/tripadvisor-scraper",
|
||||
"maxcopell/yelp-scraper",
|
||||
"trudax/booking-scraper",
|
||||
"petr_cermak/zillow-scraper",
|
||||
"epctex/craigslist-scraper",
|
||||
"apify/reddit-scraper",
|
||||
"apify/facebook-posts-scraper",
|
||||
"compass/google-places-api"
|
||||
],
|
||||
"enumTitles": [
|
||||
"Google Maps Scraper - Local business data",
|
||||
|
|
@ -88,10 +96,18 @@
|
|||
"Website Content Crawler - Full site content",
|
||||
"Cheerio Scraper - Structured extraction",
|
||||
"News Scraper - News articles",
|
||||
"LinkedIn Scraper - Jobs, profiles"
|
||||
"LinkedIn Scraper - Jobs, profiles",
|
||||
"TripAdvisor Scraper - Reviews, restaurants, hotels",
|
||||
"Yelp Scraper - Business reviews, ratings",
|
||||
"Booking.com Scraper - Hotels, accommodations",
|
||||
"Zillow Scraper - Real estate listings",
|
||||
"Craigslist Scraper - Classifieds, listings",
|
||||
"Reddit Scraper - Posts, comments, subreddits",
|
||||
"Facebook Posts Scraper - Posts, engagement",
|
||||
"Google Places API - Places, reviews"
|
||||
],
|
||||
"sectionCaption": "Actor Integration",
|
||||
"sectionDescription": "One-click integration with top Apify actors for RAG/memory use cases"
|
||||
"sectionCaption": "Actor Integration (21 Actors)",
|
||||
"sectionDescription": "One-click integration with 21 top Apify actors for RAG/memory use cases"
|
||||
},
|
||||
"integrateRunId": {
|
||||
"title": "Run ID",
|
||||
|
|
@ -116,18 +132,24 @@
|
|||
"useTemplate": {
|
||||
"title": "Use Case Template",
|
||||
"type": "string",
|
||||
"description": "Pre-built template for common use cases. Used in 'template' mode.",
|
||||
"description": "Pre-built template for common use cases. 12 templates available. Used in 'template' mode.",
|
||||
"editor": "select",
|
||||
"enum": ["lead-intelligence", "competitor-monitor", "support-knowledge", "research-assistant", "content-library", "product-catalog"],
|
||||
"enum": ["lead-intelligence", "competitor-monitor", "support-knowledge", "research-assistant", "content-library", "product-catalog", "review-aggregator", "price-tracker", "social-listening", "talent-sourcing", "real-estate-intel", "travel-planner"],
|
||||
"enumTitles": [
|
||||
"Lead Intelligence - Sales teams memorizing prospect data",
|
||||
"Competitor Monitor - Track competitor mentions/changes",
|
||||
"Support Knowledge - Customer support RAG system",
|
||||
"Research Assistant - Academic/market research",
|
||||
"Content Library - Content creators' reference",
|
||||
"Product Catalog - E-commerce product memory"
|
||||
"Product Catalog - E-commerce product memory",
|
||||
"Review Aggregator - Aggregate reviews from multiple platforms",
|
||||
"Price Tracker - Monitor prices for competitive intelligence",
|
||||
"Social Listening - Monitor social conversations and trends",
|
||||
"Talent Sourcing - Recruit candidates from job platforms",
|
||||
"Real Estate Intel - Property market analysis",
|
||||
"Travel Planner - Hotels, restaurants, activities data"
|
||||
],
|
||||
"sectionCaption": "Use Case Templates",
|
||||
"sectionCaption": "Use Case Templates (12 Templates)",
|
||||
"sectionDescription": "Pre-configured templates for common data workflows"
|
||||
},
|
||||
"schema": {
|
||||
|
|
@ -183,7 +205,30 @@
|
|||
"title": "Generate Embeddings",
|
||||
"type": "boolean",
|
||||
"description": "Generate vector embeddings for all output records (useful for RAG systems)",
|
||||
"default": false
|
||||
"default": false,
|
||||
"sectionCaption": "ONNX Embeddings",
|
||||
"sectionDescription": "Semantic embeddings powered by ONNX Runtime (Hugging Face Transformers.js)"
|
||||
},
|
||||
"useOnnxEmbeddings": {
|
||||
"title": "Use ONNX Semantic Embeddings",
|
||||
"type": "boolean",
|
||||
"description": "Use real semantic embeddings via ONNX (slower but more accurate) vs random vectors (fast for testing)",
|
||||
"default": true
|
||||
},
|
||||
"embeddingModel": {
|
||||
"title": "ONNX Embedding Model",
|
||||
"type": "string",
|
||||
"description": "Choose embedding model. Smaller models are faster, larger models are more accurate.",
|
||||
"editor": "select",
|
||||
"default": "all-MiniLM-L6-v2",
|
||||
"enum": ["all-MiniLM-L6-v2", "bge-small-en-v1.5", "all-mpnet-base-v2", "e5-small-v2", "gte-small"],
|
||||
"enumTitles": [
|
||||
"all-MiniLM-L6-v2 (384d, Fast, Good) - Recommended",
|
||||
"bge-small-en-v1.5 (384d, Fast, Excellent)",
|
||||
"all-mpnet-base-v2 (768d, Medium, Excellent)",
|
||||
"e5-small-v2 (384d, Fast, Very Good)",
|
||||
"gte-small (384d, Fast, Very Good)"
|
||||
]
|
||||
},
|
||||
"simulationMode": {
|
||||
"title": "Long-Running Simulation",
|
||||
|
|
|
|||
|
|
@ -1,24 +1,24 @@
|
|||
# AI Synthetic Data Generator - MCP Server & Actor Integration
|
||||
# AI Synthetic Data Generator - ONNX Embeddings, MCP & 21 Actor Integrations
|
||||
|
||||
**Generate unlimited synthetic data** grounded in real-world patterns. **One-click integration** with 13 popular Apify web scrapers (Google Maps, Instagram, TikTok, Amazon, LinkedIn) lets you transform real scraped data into AI-ready formats for RAG systems, agent memory, and model training.
|
||||
**Generate unlimited synthetic data** with **ONNX-powered semantic embeddings** (all-MiniLM-L6-v2, bge-small). **One-click integration** with 21 popular Apify web scrapers (Google Maps, Instagram, TikTok, Amazon, LinkedIn, Reddit, Yelp, TripAdvisor, Zillow, Booking.com + more). 12 use-case templates. TRM/SONA self-learning. MCP server support.
|
||||
|
||||
**Why grounding matters:** Pure synthetic data can drift from reality. By integrating with live Apify scrapers, your synthetic data inherits real naming conventions, price distributions, engagement patterns, and business characteristics - making your AI models and tests far more realistic.
|
||||
|
||||
[](https://apify.com/ruv/ai-synthetic-data-generator)
|
||||
[](https://modelcontextprotocol.io)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://github.com/ruvnet/ruvector)
|
||||
[](https://github.com/ruvnet/ruvector)
|
||||
|
||||
## What's New in v2.2
|
||||
## What's New in v2.5
|
||||
|
||||
| Feature | Description |
|
||||
|---------|-------------|
|
||||
| **One-Click Scraper Integration** | Ground synthetic data with real patterns from Google Maps, Instagram, TikTok, YouTube, Amazon, LinkedIn, and 7 more scrapers |
|
||||
| **ONNX Semantic Embeddings** | Real semantic embeddings via Hugging Face Transformers.js (all-MiniLM-L6-v2, bge-small, all-mpnet-base-v2, e5-small, gte-small) |
|
||||
| **21 Actor Integrations** | Added Reddit, Yelp, TripAdvisor, Zillow, Booking.com, Craigslist, Facebook, Google Places |
|
||||
| **12 Use Case Templates** | Added Review Aggregator, Price Tracker, Social Listening, Talent Sourcing, Real Estate Intel, Travel Planner |
|
||||
| **MCP Server** | Use as AI agent tool (Claude, GPT) via Model Context Protocol |
|
||||
| **6 Use Case Templates** | Lead Intelligence, Competitor Monitor, Support RAG, and more |
|
||||
| **Enhanced Grounding** | Transform scraped data into AI-ready formats that preserve real-world distributions |
|
||||
| **Webhook Support** | POST results to your endpoint for async workflows |
|
||||
| **Output Formats** | JSON, JSONL, CSV export options |
|
||||
| **TRM/SONA Self-Learning** | 7M parameter recursive reasoning with 3-tier learning |
|
||||
| **Output Formats** | JSON, JSONL, CSV export with webhook support |
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -26,9 +26,10 @@
|
|||
|
||||
| Feature | Description |
|
||||
|---------|-------------|
|
||||
| **Scraper Integration** | One-click grounding with 13 popular Apify scrapers for realistic data |
|
||||
| **ONNX Embeddings** | 5 semantic embedding models via Transformers.js for RAG-ready vectors |
|
||||
| **21 Actor Integrations** | One-click grounding with 21 popular Apify scrapers |
|
||||
| **12 Templates** | Pre-built workflows for common use cases |
|
||||
| **MCP Server** | Integrate with Claude Code, GPT, and AI agents |
|
||||
| **6 Templates** | Pre-built workflows for common use cases |
|
||||
| **TRM** | 7M parameter recursive reasoning (83% on GSM8K) |
|
||||
| **SONA** | 3-tier self-learning (Instant/Background/Deep) |
|
||||
| **EWC++** | Pattern preservation across generations (lambda=2000) |
|
||||
|
|
@ -92,7 +93,35 @@ Once connected, you get these tools in your AI agent:
|
|||
|
||||
---
|
||||
|
||||
## One-Click Actor Integration
|
||||
## ONNX Semantic Embeddings
|
||||
|
||||
Generate real semantic embeddings for RAG systems using ONNX Runtime and Hugging Face Transformers.js.
|
||||
|
||||
### Available Models
|
||||
|
||||
| Model | Dimensions | Speed | Quality | Use Case |
|
||||
|-------|------------|-------|---------|----------|
|
||||
| **all-MiniLM-L6-v2** | 384 | Fast | Good | General purpose (recommended) |
|
||||
| **bge-small-en-v1.5** | 384 | Fast | Excellent | High-quality retrieval |
|
||||
| **all-mpnet-base-v2** | 768 | Medium | Excellent | Maximum accuracy |
|
||||
| **e5-small-v2** | 384 | Fast | Very Good | Balanced performance |
|
||||
| **gte-small** | 384 | Fast | Very Good | Efficient retrieval |
|
||||
|
||||
### Example: Generate with ONNX Embeddings
|
||||
|
||||
```json
|
||||
{
|
||||
"dataType": "ecommerce",
|
||||
"count": 100,
|
||||
"generateEmbeddings": true,
|
||||
"useOnnxEmbeddings": true,
|
||||
"embeddingModel": "all-MiniLM-L6-v2"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## One-Click Actor Integration (21 Actors)
|
||||
|
||||
Transform data from popular Apify scrapers into AI-ready format.
|
||||
|
||||
|
|
@ -113,6 +142,14 @@ Transform data from popular Apify scrapers into AI-ready format.
|
|||
| **apify/web-scraper** | General | Any web page data |
|
||||
| **apify/cheerio-scraper** | General | Structured extraction |
|
||||
| **apify/news-scraper** | News | Articles, authors, sources |
|
||||
| **trudax/tripadvisor-scraper** | Reviews | Hotels, restaurants, reviews |
|
||||
| **maxcopell/yelp-scraper** | Reviews | Business reviews, ratings |
|
||||
| **trudax/booking-scraper** | Travel | Hotels, accommodations, prices |
|
||||
| **petr_cermak/zillow-scraper** | Real Estate | Property listings, prices |
|
||||
| **epctex/craigslist-scraper** | Classifieds | Listings, local posts |
|
||||
| **apify/reddit-scraper** | Social Media | Posts, comments, subreddits |
|
||||
| **apify/facebook-posts-scraper** | Social Media | Posts, engagement |
|
||||
| **compass/google-places-api** | Local Business | Places, reviews, details |
|
||||
|
||||
### Example: Integrate Google Maps Data
|
||||
|
||||
|
|
@ -128,7 +165,7 @@ Transform data from popular Apify scrapers into AI-ready format.
|
|||
|
||||
---
|
||||
|
||||
## Use Case Templates
|
||||
## Use Case Templates (12 Templates)
|
||||
|
||||
One-click deployment for common AI/RAG scenarios.
|
||||
|
||||
|
|
@ -140,6 +177,12 @@ One-click deployment for common AI/RAG scenarios.
|
|||
| **research-assistant** | Academic/market research | Researchers | Google Search, News, Content |
|
||||
| **content-library** | Content creators' reference | Creators | Instagram, TikTok, YouTube |
|
||||
| **product-catalog** | E-commerce product memory | E-commerce | Amazon, Shopify, Google Maps |
|
||||
| **review-aggregator** | Aggregate reviews from platforms | Product Managers | TripAdvisor, Yelp, Google Maps |
|
||||
| **price-tracker** | Monitor prices across sites | Pricing Teams | Amazon, Zillow, Booking.com |
|
||||
| **social-listening** | Monitor social conversations | Social Media, PR | Reddit, Twitter, Facebook |
|
||||
| **talent-sourcing** | Recruit from job platforms | Recruiters, HR | LinkedIn, Craigslist |
|
||||
| **real-estate-intel** | Property market analysis | Real Estate, Investors | Zillow, Google Maps, Craigslist |
|
||||
| **travel-planner** | Hotels, restaurants, activities | Travel Agents | TripAdvisor, Booking.com, Google Maps |
|
||||
|
||||
### Example: Lead Intelligence Template
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
{
|
||||
"name": "agentic-synth-apify-actor",
|
||||
"version": "2.2.0",
|
||||
"description": "AI Synthetic Data Generator with TRM/SONA self-learning, MCP server, and Apify actor integrations",
|
||||
"version": "2.5.0",
|
||||
"description": "AI Synthetic Data Generator with ONNX embeddings, TRM/SONA self-learning, MCP server, and 21 Apify actor integrations",
|
||||
"main": "src/main.js",
|
||||
"type": "module",
|
||||
"bin": {
|
||||
|
|
@ -19,6 +19,7 @@
|
|||
"@google/generative-ai": "^0.24.1",
|
||||
"@modelcontextprotocol/sdk": "^1.0.0",
|
||||
"@ruvector/ruvllm": "^0.2.3",
|
||||
"@xenova/transformers": "^2.17.2",
|
||||
"apify": "^3.5.2"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
|
|
|
|||
96
examples/apify/agentic-synth/src/embeddings.js
Normal file
96
examples/apify/agentic-synth/src/embeddings.js
Normal file
|
|
@ -0,0 +1,96 @@
|
|||
/**
|
||||
* ONNX-based Embeddings Module
|
||||
* Uses @xenova/transformers (Hugging Face Transformers.js) for ONNX Runtime embeddings
|
||||
*/
|
||||
|
||||
import { log } from 'apify';
|
||||
|
||||
let embeddingPipeline = null;
|
||||
let currentModel = null;
|
||||
|
||||
export const EMBEDDING_MODELS = {
|
||||
'all-MiniLM-L6-v2': { id: 'Xenova/all-MiniLM-L6-v2', dimensions: 384, speed: 'fast', quality: 'good' },
|
||||
'bge-small-en-v1.5': { id: 'Xenova/bge-small-en-v1.5', dimensions: 384, speed: 'fast', quality: 'excellent' },
|
||||
'all-mpnet-base-v2': { id: 'Xenova/all-mpnet-base-v2', dimensions: 768, speed: 'medium', quality: 'excellent' },
|
||||
'e5-small-v2': { id: 'Xenova/e5-small-v2', dimensions: 384, speed: 'fast', quality: 'very-good' },
|
||||
'gte-small': { id: 'Xenova/gte-small', dimensions: 384, speed: 'fast', quality: 'very-good' }
|
||||
};
|
||||
|
||||
export async function initEmbeddingPipeline(modelName = 'all-MiniLM-L6-v2') {
|
||||
const modelConfig = EMBEDDING_MODELS[modelName];
|
||||
if (!modelConfig) throw new Error(`Unknown model: ${modelName}`);
|
||||
if (embeddingPipeline && currentModel === modelName) return embeddingPipeline;
|
||||
|
||||
const { pipeline } = await import('@xenova/transformers');
|
||||
log.info(`Loading ONNX embedding model: ${modelConfig.id}...`);
|
||||
embeddingPipeline = await pipeline('feature-extraction', modelConfig.id, { quantized: true });
|
||||
currentModel = modelName;
|
||||
return embeddingPipeline;
|
||||
}
|
||||
|
||||
export async function generateEmbedding(text, options = {}) {
|
||||
const { modelName = 'all-MiniLM-L6-v2', normalize = true } = options;
|
||||
const pipe = await initEmbeddingPipeline(modelName);
|
||||
const output = await pipe(text.substring(0, 8000), { pooling: 'mean', normalize });
|
||||
return Array.from(output.data);
|
||||
}
|
||||
|
||||
export async function generateEmbeddingsBatch(texts, options = {}) {
|
||||
const { modelName = 'all-MiniLM-L6-v2', batchSize = 32, onProgress = null } = options;
|
||||
const pipe = await initEmbeddingPipeline(modelName);
|
||||
const embeddings = [];
|
||||
|
||||
for (let i = 0; i < texts.length; i += batchSize) {
|
||||
const batch = texts.slice(i, i + batchSize);
|
||||
const batchEmbeddings = await Promise.all(
|
||||
batch.map(async (text) => {
|
||||
const output = await pipe((text || '').substring(0, 8000), { pooling: 'mean', normalize: true });
|
||||
return Array.from(output.data);
|
||||
})
|
||||
);
|
||||
embeddings.push(...batchEmbeddings);
|
||||
if (onProgress) onProgress({ processed: Math.min(i + batchSize, texts.length), total: texts.length });
|
||||
}
|
||||
return embeddings;
|
||||
}
|
||||
|
||||
export async function addEmbeddingsToRecords(records, options = {}) {
|
||||
const { modelName = 'all-MiniLM-L6-v2', textFields = ['title', 'description', 'text', 'content', 'caption', 'body', 'name'] } = options;
|
||||
if (!records?.length) return records;
|
||||
|
||||
const modelConfig = EMBEDDING_MODELS[modelName];
|
||||
log.info(`Generating ONNX embeddings for ${records.length} records with ${modelName}`);
|
||||
|
||||
const texts = records.map(record => {
|
||||
const parts = textFields.map(f => {
|
||||
const v = record[f] || record.data?.[f];
|
||||
return typeof v === 'string' ? v : Array.isArray(v) ? v.join(' ') : '';
|
||||
}).filter(Boolean);
|
||||
return parts.join(' ') || 'empty';
|
||||
});
|
||||
|
||||
const embeddings = await generateEmbeddingsBatch(texts, { modelName });
|
||||
return records.map((record, i) => ({ ...record, embedding: embeddings[i], embeddingModel: modelName, embeddingDimensions: modelConfig.dimensions }));
|
||||
}
|
||||
|
||||
export function generateRandomEmbedding(dimensions, random = Math.random) {
|
||||
const embedding = [];
|
||||
let norm = 0;
|
||||
for (let i = 0; i < dimensions; i++) {
|
||||
const val = random() * 2 - 1;
|
||||
embedding.push(val);
|
||||
norm += val * val;
|
||||
}
|
||||
norm = Math.sqrt(norm);
|
||||
return embedding.map(v => Math.round((v / norm) * 1000000) / 1000000);
|
||||
}
|
||||
|
||||
export function cosineSimilarity(a, b) {
|
||||
let dot = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
return dot / (Math.sqrt(normA) * Math.sqrt(normB));
|
||||
}
|
||||
|
|
@ -98,6 +98,62 @@ export const SUPPORTED_ACTORS = {
|
|||
category: 'professional',
|
||||
defaultFields: ['title', 'company', 'location', 'description', 'salary'],
|
||||
transform: transformLinkedIn
|
||||
},
|
||||
|
||||
// Reviews & Local
|
||||
'trudax/tripadvisor-scraper': {
|
||||
name: 'TripAdvisor Scraper',
|
||||
category: 'reviews',
|
||||
defaultFields: ['name', 'rating', 'reviewCount', 'address', 'priceLevel', 'cuisine'],
|
||||
transform: transformTripAdvisor
|
||||
},
|
||||
'maxcopell/yelp-scraper': {
|
||||
name: 'Yelp Scraper',
|
||||
category: 'reviews',
|
||||
defaultFields: ['name', 'rating', 'reviewCount', 'address', 'categories', 'phone'],
|
||||
transform: transformYelp
|
||||
},
|
||||
'trudax/booking-scraper': {
|
||||
name: 'Booking.com Scraper',
|
||||
category: 'travel',
|
||||
defaultFields: ['name', 'rating', 'price', 'location', 'amenities', 'reviewScore'],
|
||||
transform: transformBooking
|
||||
},
|
||||
|
||||
// Real Estate
|
||||
'petr_cermak/zillow-scraper': {
|
||||
name: 'Zillow Scraper',
|
||||
category: 'real-estate',
|
||||
defaultFields: ['address', 'price', 'bedrooms', 'bathrooms', 'sqft', 'propertyType'],
|
||||
transform: transformZillow
|
||||
},
|
||||
'epctex/craigslist-scraper': {
|
||||
name: 'Craigslist Scraper',
|
||||
category: 'classifieds',
|
||||
defaultFields: ['title', 'price', 'location', 'description', 'category', 'postedAt'],
|
||||
transform: transformCraigslist
|
||||
},
|
||||
|
||||
// Social Platforms
|
||||
'apify/reddit-scraper': {
|
||||
name: 'Reddit Scraper',
|
||||
category: 'social-media',
|
||||
defaultFields: ['title', 'text', 'subreddit', 'score', 'comments', 'author'],
|
||||
transform: transformReddit
|
||||
},
|
||||
'apify/facebook-posts-scraper': {
|
||||
name: 'Facebook Posts Scraper',
|
||||
category: 'social-media',
|
||||
defaultFields: ['text', 'likes', 'comments', 'shares', 'author', 'timestamp'],
|
||||
transform: transformFacebook
|
||||
},
|
||||
|
||||
// Places & Maps
|
||||
'compass/google-places-api': {
|
||||
name: 'Google Places API',
|
||||
category: 'local-business',
|
||||
defaultFields: ['name', 'rating', 'address', 'phone', 'website', 'types', 'priceLevel'],
|
||||
transform: transformGooglePlaces
|
||||
}
|
||||
};
|
||||
|
||||
|
|
@ -217,6 +273,128 @@ export const USE_CASE_TEMPLATES = {
|
|||
recommendedAlternatives: 'array<string>',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'review-aggregator': {
|
||||
name: 'Review Aggregator',
|
||||
description: 'Aggregate and analyze reviews from multiple platforms',
|
||||
targetUsers: ['Product Managers', 'Brand Managers', 'Customer Experience'],
|
||||
suggestedActors: ['trudax/tripadvisor-scraper', 'maxcopell/yelp-scraper', 'apify/google-maps-scraper'],
|
||||
memorizeFields: ['name', 'rating', 'reviewCount', 'text', 'sentiment', 'categories'],
|
||||
enrichWith: ['sentiment_score', 'common_themes', 'rating_trend'],
|
||||
outputFormat: {
|
||||
entityId: 'string',
|
||||
name: 'string',
|
||||
averageRating: 'number',
|
||||
totalReviews: 'number',
|
||||
platforms: 'array<object>',
|
||||
sentimentAnalysis: 'object',
|
||||
topPraises: 'array<string>',
|
||||
topComplaints: 'array<string>',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'price-tracker': {
|
||||
name: 'Price Tracker',
|
||||
description: 'Monitor prices across platforms for competitive intelligence',
|
||||
targetUsers: ['Pricing Teams', 'Buyers', 'Resellers'],
|
||||
suggestedActors: ['apify/amazon-scraper', 'petr_cermak/zillow-scraper', 'trudax/booking-scraper'],
|
||||
memorizeFields: ['title', 'price', 'currency', 'availability', 'seller', 'timestamp'],
|
||||
enrichWith: ['price_history', 'price_alerts', 'competitor_comparison'],
|
||||
outputFormat: {
|
||||
productId: 'string',
|
||||
title: 'string',
|
||||
currentPrice: 'number',
|
||||
priceHistory: 'array<object>',
|
||||
lowestPrice: 'number',
|
||||
highestPrice: 'number',
|
||||
priceChange: 'number',
|
||||
competitors: 'array<object>',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'social-listening': {
|
||||
name: 'Social Listening',
|
||||
description: 'Monitor social conversations about brands, topics, and trends',
|
||||
targetUsers: ['Social Media Managers', 'PR Teams', 'Brand Managers'],
|
||||
suggestedActors: ['apify/reddit-scraper', 'apify/twitter-scraper', 'apify/facebook-posts-scraper'],
|
||||
memorizeFields: ['text', 'author', 'engagement', 'sentiment', 'platform', 'timestamp'],
|
||||
enrichWith: ['sentiment_analysis', 'influencer_score', 'viral_potential'],
|
||||
outputFormat: {
|
||||
postId: 'string',
|
||||
platform: 'string',
|
||||
content: 'string',
|
||||
author: 'object',
|
||||
engagement: 'object',
|
||||
sentiment: 'number (-1 to 1)',
|
||||
mentions: 'array<string>',
|
||||
hashtags: 'array<string>',
|
||||
viralScore: 'number (1-100)',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'talent-sourcing': {
|
||||
name: 'Talent Sourcing',
|
||||
description: 'Recruit and source candidates from job platforms',
|
||||
targetUsers: ['Recruiters', 'HR Teams', 'Talent Acquisition'],
|
||||
suggestedActors: ['apify/linkedin-scraper', 'epctex/craigslist-scraper'],
|
||||
memorizeFields: ['title', 'company', 'location', 'skills', 'experience', 'salary'],
|
||||
enrichWith: ['skill_match', 'culture_fit', 'availability'],
|
||||
outputFormat: {
|
||||
candidateId: 'string',
|
||||
name: 'string',
|
||||
currentRole: 'string',
|
||||
company: 'string',
|
||||
skills: 'array<string>',
|
||||
experience: 'number',
|
||||
location: 'string',
|
||||
matchScore: 'number (1-100)',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'real-estate-intel': {
|
||||
name: 'Real Estate Intelligence',
|
||||
description: 'Market analysis and property intelligence for real estate',
|
||||
targetUsers: ['Real Estate Agents', 'Investors', 'Property Managers'],
|
||||
suggestedActors: ['petr_cermak/zillow-scraper', 'apify/google-maps-scraper', 'epctex/craigslist-scraper'],
|
||||
memorizeFields: ['address', 'price', 'sqft', 'bedrooms', 'bathrooms', 'propertyType'],
|
||||
enrichWith: ['market_trends', 'comparable_sales', 'neighborhood_score'],
|
||||
outputFormat: {
|
||||
propertyId: 'string',
|
||||
address: 'string',
|
||||
price: 'number',
|
||||
pricePerSqft: 'number',
|
||||
propertyType: 'string',
|
||||
specs: 'object',
|
||||
marketAnalysis: 'object',
|
||||
comparables: 'array<object>',
|
||||
investmentScore: 'number (1-100)',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
},
|
||||
|
||||
'travel-planner': {
|
||||
name: 'Travel Planner',
|
||||
description: 'Plan trips with aggregated hotel, restaurant, and activity data',
|
||||
targetUsers: ['Travel Agents', 'Travelers', 'Tourism Boards'],
|
||||
suggestedActors: ['trudax/tripadvisor-scraper', 'trudax/booking-scraper', 'apify/google-maps-scraper'],
|
||||
memorizeFields: ['name', 'rating', 'price', 'location', 'amenities', 'reviews'],
|
||||
enrichWith: ['booking_availability', 'best_time_to_visit', 'local_tips'],
|
||||
outputFormat: {
|
||||
placeId: 'string',
|
||||
name: 'string',
|
||||
type: 'string (hotel, restaurant, attraction)',
|
||||
rating: 'number',
|
||||
priceRange: 'string',
|
||||
location: 'object',
|
||||
highlights: 'array<string>',
|
||||
reviews: 'array<object>',
|
||||
embedding: 'array<number>'
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
|
|
@ -515,6 +693,225 @@ function transformLinkedIn(item) {
|
|||
};
|
||||
}
|
||||
|
||||
function transformTripAdvisor(item) {
|
||||
return {
|
||||
id: item.id || item.locationId || generateId(),
|
||||
source: 'tripadvisor',
|
||||
name: item.name || item.title,
|
||||
type: item.type || item.category,
|
||||
rating: item.rating || item.overallRating,
|
||||
reviewCount: item.reviewCount || item.numberOfReviews,
|
||||
priceLevel: item.priceLevel || item.priceRange,
|
||||
address: {
|
||||
full: item.address || item.addressObj?.street1,
|
||||
city: item.city || item.addressObj?.city,
|
||||
country: item.country || item.addressObj?.country
|
||||
},
|
||||
cuisine: item.cuisine || item.cuisines || [],
|
||||
features: item.features || item.amenities || [],
|
||||
awards: item.awards || [],
|
||||
photos: (item.photos || []).slice(0, 5).map(p => p.url || p),
|
||||
url: item.url || item.webUrl,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformYelp(item) {
|
||||
return {
|
||||
id: item.id || item.businessId || generateId(),
|
||||
source: 'yelp',
|
||||
name: item.name || item.businessName,
|
||||
rating: item.rating,
|
||||
reviewCount: item.reviewCount || item.review_count,
|
||||
priceLevel: item.price || item.priceRange,
|
||||
address: {
|
||||
full: item.address || item.location?.display_address?.join(', '),
|
||||
street: item.location?.address1,
|
||||
city: item.location?.city,
|
||||
state: item.location?.state,
|
||||
zip: item.location?.zip_code
|
||||
},
|
||||
phone: item.phone || item.display_phone,
|
||||
categories: (item.categories || []).map(c => c.title || c),
|
||||
hours: item.hours || item.businessHours,
|
||||
photos: (item.photos || []).slice(0, 5),
|
||||
isClaimed: item.is_claimed,
|
||||
url: item.url,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformBooking(item) {
|
||||
return {
|
||||
id: item.id || item.hotelId || generateId(),
|
||||
source: 'booking',
|
||||
name: item.name || item.hotelName,
|
||||
type: item.type || item.accommodationType || 'hotel',
|
||||
rating: item.rating || item.reviewScore,
|
||||
reviewScore: item.reviewScore || item.score,
|
||||
reviewCount: item.reviewCount || item.numberOfReviews,
|
||||
stars: item.stars || item.starRating,
|
||||
price: {
|
||||
amount: item.price || item.priceAmount,
|
||||
currency: item.currency || 'USD',
|
||||
perNight: item.pricePerNight || item.price
|
||||
},
|
||||
location: {
|
||||
address: item.address,
|
||||
city: item.city,
|
||||
country: item.country,
|
||||
lat: item.latitude || item.location?.lat,
|
||||
lng: item.longitude || item.location?.lng
|
||||
},
|
||||
amenities: item.amenities || item.facilities || [],
|
||||
photos: (item.photos || []).slice(0, 5).map(p => p.url || p),
|
||||
url: item.url,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformZillow(item) {
|
||||
return {
|
||||
id: item.zpid || item.id || generateId(),
|
||||
source: 'zillow',
|
||||
address: {
|
||||
full: item.address || item.streetAddress,
|
||||
street: item.streetAddress,
|
||||
city: item.city,
|
||||
state: item.state,
|
||||
zip: item.zipcode
|
||||
},
|
||||
price: item.price || item.zestimate,
|
||||
zestimate: item.zestimate,
|
||||
rentZestimate: item.rentZestimate,
|
||||
propertyType: item.homeType || item.propertyType,
|
||||
status: item.homeStatus || item.status,
|
||||
specs: {
|
||||
bedrooms: item.bedrooms || item.beds,
|
||||
bathrooms: item.bathrooms || item.baths,
|
||||
sqft: item.livingArea || item.sqft,
|
||||
lotSize: item.lotSize || item.lotAreaValue,
|
||||
yearBuilt: item.yearBuilt
|
||||
},
|
||||
features: item.resoFacts?.atAGlanceFacts || [],
|
||||
priceHistory: item.priceHistory || [],
|
||||
taxHistory: item.taxHistory || [],
|
||||
photos: (item.photos || item.hiResImageLink || []).slice(0, 5),
|
||||
url: item.url || item.hdpUrl,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformCraigslist(item) {
|
||||
return {
|
||||
id: item.id || item.postId || generateId(),
|
||||
source: 'craigslist',
|
||||
title: item.title || item.postTitle,
|
||||
price: item.price,
|
||||
category: item.category || item.section,
|
||||
subcategory: item.subcategory,
|
||||
location: {
|
||||
area: item.location || item.hood,
|
||||
city: item.city,
|
||||
region: item.region
|
||||
},
|
||||
description: item.description || item.body,
|
||||
attributes: item.attributes || {},
|
||||
images: (item.images || item.pics || []).slice(0, 5),
|
||||
postedAt: item.datetime || item.postedAt,
|
||||
updatedAt: item.updated,
|
||||
url: item.url || item.postUrl,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformReddit(item) {
|
||||
return {
|
||||
id: item.id || item.postId || generateId(),
|
||||
source: 'reddit',
|
||||
type: item.type || (item.isSelf ? 'text' : 'link'),
|
||||
title: item.title,
|
||||
text: item.selftext || item.body || item.text,
|
||||
subreddit: item.subreddit || item.subredditName,
|
||||
author: {
|
||||
username: item.author || item.authorName,
|
||||
id: item.authorId
|
||||
},
|
||||
engagement: {
|
||||
score: item.score || item.ups - (item.downs || 0),
|
||||
upvotes: item.ups,
|
||||
downvotes: item.downs,
|
||||
comments: item.numComments || item.num_comments,
|
||||
awards: item.totalAwards || item.total_awards_received
|
||||
},
|
||||
flair: item.linkFlair || item.link_flair_text,
|
||||
nsfw: item.over18 || item.over_18 || false,
|
||||
spoiler: item.spoiler || false,
|
||||
url: item.url || `https://reddit.com${item.permalink}`,
|
||||
mediaUrl: item.mediaUrl || item.url_overridden_by_dest,
|
||||
createdAt: item.created || item.createdUtc,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformFacebook(item) {
|
||||
return {
|
||||
id: item.id || item.postId || generateId(),
|
||||
source: 'facebook',
|
||||
type: item.type || 'post',
|
||||
text: item.text || item.message || item.content,
|
||||
author: {
|
||||
name: item.authorName || item.user?.name,
|
||||
id: item.authorId || item.user?.id,
|
||||
url: item.authorUrl || item.user?.url
|
||||
},
|
||||
engagement: {
|
||||
likes: item.likes || item.likesCount,
|
||||
comments: item.comments || item.commentsCount,
|
||||
shares: item.shares || item.sharesCount,
|
||||
reactions: item.reactions || {}
|
||||
},
|
||||
media: {
|
||||
images: item.images || [],
|
||||
videos: item.videos || [],
|
||||
links: item.links || []
|
||||
},
|
||||
hashtags: extractHashtags(item.text || item.message),
|
||||
mentions: extractMentions(item.text || item.message),
|
||||
timestamp: item.time || item.timestamp || item.createdAt,
|
||||
url: item.url || item.postUrl,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
function transformGooglePlaces(item) {
|
||||
return {
|
||||
id: item.place_id || item.placeId || generateId(),
|
||||
source: 'google-places',
|
||||
name: item.name,
|
||||
rating: item.rating,
|
||||
reviewCount: item.user_ratings_total || item.reviewCount,
|
||||
priceLevel: item.price_level || item.priceLevel,
|
||||
address: item.formatted_address || item.address,
|
||||
phone: item.formatted_phone_number || item.phone,
|
||||
website: item.website,
|
||||
types: item.types || [],
|
||||
location: {
|
||||
lat: item.geometry?.location?.lat || item.lat,
|
||||
lng: item.geometry?.location?.lng || item.lng
|
||||
},
|
||||
hours: item.opening_hours || item.hours,
|
||||
photos: (item.photos || []).slice(0, 5).map(p => p.photo_reference || p),
|
||||
reviews: (item.reviews || []).slice(0, 5).map(r => ({
|
||||
text: r.text,
|
||||
rating: r.rating,
|
||||
author: r.author_name
|
||||
})),
|
||||
url: item.url,
|
||||
scrapedAt: item.scrapedAt || new Date().toISOString()
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// UTILITY FUNCTIONS
|
||||
// ============================================
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import { Actor, log } from 'apify';
|
|||
import { GoogleGenerativeAI } from '@google/generative-ai';
|
||||
import { createRequire } from 'module';
|
||||
import { integrateActorData, SUPPORTED_ACTORS, USE_CASE_TEMPLATES, getTemplate, listSupportedActors, listTemplates } from './integrations.js';
|
||||
import { addEmbeddingsToRecords, generateRandomEmbedding, EMBEDDING_MODELS } from './embeddings.js';
|
||||
|
||||
// CJS import workaround for RuvLLM native extension
|
||||
const require = createRequire(import.meta.url);
|
||||
|
|
@ -58,10 +59,13 @@ try {
|
|||
sonaEnabled = true,
|
||||
ewcLambda = 2000,
|
||||
patternThreshold = 0.7,
|
||||
sonaLearningTiers = ['instant', 'background']
|
||||
sonaLearningTiers = ['instant', 'background'],
|
||||
// ONNX Embedding parameters
|
||||
useOnnxEmbeddings = true,
|
||||
embeddingModel = 'all-MiniLM-L6-v2'
|
||||
} = input;
|
||||
|
||||
log.info('AI Synthetic Data Generator v2.2 with TRM/SONA', { mode, dataType, count, provider, model, sonaEnabled });
|
||||
log.info('AI Synthetic Data Generator v2.5 with ONNX Embeddings & TRM/SONA', { mode, dataType, count, provider, model, sonaEnabled, useOnnxEmbeddings, embeddingModel });
|
||||
|
||||
// Initialize SONA if available and enabled
|
||||
if (ruvllm && sonaEnabled) {
|
||||
|
|
@ -274,33 +278,42 @@ try {
|
|||
// EMBEDDING GENERATION (optional)
|
||||
// ============================================
|
||||
if (generateEmbeddings && generatedData.length > 0) {
|
||||
log.info(`Generating embeddings with ${embeddingDimensions} dimensions...`);
|
||||
const modelConfig = EMBEDDING_MODELS[embeddingModel] || EMBEDDING_MODELS['all-MiniLM-L6-v2'];
|
||||
const effectiveDimensions = useOnnxEmbeddings ? modelConfig.dimensions : embeddingDimensions;
|
||||
|
||||
const random = createSeededRandom(seed);
|
||||
|
||||
generatedData = generatedData.map((item, idx) => {
|
||||
// Generate normalized random embedding
|
||||
const embedding = [];
|
||||
let norm = 0;
|
||||
|
||||
for (let j = 0; j < embeddingDimensions; j++) {
|
||||
const val = random() * 2 - 1;
|
||||
embedding.push(val);
|
||||
norm += val * val;
|
||||
}
|
||||
|
||||
norm = Math.sqrt(norm);
|
||||
for (let j = 0; j < embeddingDimensions; j++) {
|
||||
embedding[j] = Math.round((embedding[j] / norm) * 1000000) / 1000000;
|
||||
}
|
||||
|
||||
return {
|
||||
...item,
|
||||
embedding,
|
||||
embeddingDimensions
|
||||
};
|
||||
log.info(`Generating embeddings with ${effectiveDimensions} dimensions...`, {
|
||||
useOnnx: useOnnxEmbeddings,
|
||||
model: useOnnxEmbeddings ? embeddingModel : 'random'
|
||||
});
|
||||
|
||||
if (useOnnxEmbeddings) {
|
||||
// Use ONNX-powered semantic embeddings
|
||||
try {
|
||||
generatedData = await addEmbeddingsToRecords(generatedData, { modelName: embeddingModel });
|
||||
log.info(`Added ONNX embeddings using ${embeddingModel} model`);
|
||||
await Actor.charge({ eventName: 'onnx-embedding-generation', count: generatedData.length });
|
||||
} catch (e) {
|
||||
log.warning(`ONNX embedding failed: ${e.message}. Falling back to random embeddings.`);
|
||||
// Fall back to random embeddings
|
||||
const random = createSeededRandom(seed);
|
||||
generatedData = generatedData.map((item) => ({
|
||||
...item,
|
||||
embedding: generateRandomEmbedding(effectiveDimensions, random),
|
||||
embeddingModel: 'random',
|
||||
embeddingDimensions: effectiveDimensions
|
||||
}));
|
||||
}
|
||||
} else {
|
||||
// Use random embeddings (faster, for testing)
|
||||
const random = createSeededRandom(seed);
|
||||
generatedData = generatedData.map((item) => ({
|
||||
...item,
|
||||
embedding: generateRandomEmbedding(effectiveDimensions, random),
|
||||
embeddingModel: 'random',
|
||||
embeddingDimensions: effectiveDimensions
|
||||
}));
|
||||
}
|
||||
|
||||
// Charge for embedding generation
|
||||
await Actor.charge({ eventName: 'embedding-generation', count: generatedData.length });
|
||||
log.info(`Added embeddings to ${generatedData.length} records`);
|
||||
|
|
@ -476,7 +489,7 @@ try {
|
|||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'User-Agent': 'Apify-AI-Synthetic-Data-Generator/2.2'
|
||||
'User-Agent': 'Apify-AI-Synthetic-Data-Generator/2.5'
|
||||
},
|
||||
body: JSON.stringify(webhookPayload)
|
||||
});
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
{"mode":"backtest","symbols":["AAPL"]}
|
||||
|
|
@ -1371,7 +1371,12 @@ await Actor.main(async () => {
|
|||
const signals = [];
|
||||
for (const symbol of symbols) {
|
||||
const marketData = generateMarketData(symbol, lookbackPeriod, { stopLoss, takeProfit, timeframe });
|
||||
const technicalData = { rsi: TechnicalIndicators.calculateRSI(marketData.prices), macd: TechnicalIndicators.calculateMACD(marketData.prices) };
|
||||
const technicalData = {
|
||||
rsi: TechnicalIndicators.calculateRSI(marketData.prices),
|
||||
macd: TechnicalIndicators.calculateMACD(marketData.prices),
|
||||
bollinger: TechnicalIndicators.calculateBollinger(marketData.prices),
|
||||
atr: TechnicalIndicators.calculateATR(marketData.highs, marketData.lows, marketData.prices)
|
||||
};
|
||||
const features = prepareFeatures(marketData, technicalData);
|
||||
const output = neuralEngine.forward(features);
|
||||
const signal = signalGenerator.generateSignal([output[0]], marketData);
|
||||
|
|
|
|||
|
|
@ -0,0 +1,32 @@
|
|||
{
|
||||
"timestamp": "2025-12-13T19:20:02.211Z",
|
||||
"symbol": "BTC/USD",
|
||||
"price": 245.881612493563,
|
||||
"signal": "SELL",
|
||||
"confidence": 100,
|
||||
"reasons": [
|
||||
"Neural prediction: 1.93%",
|
||||
"Patterns: double_top"
|
||||
],
|
||||
"target": 233.58753186888484,
|
||||
"stopLoss": 252.02865280590206,
|
||||
"patterns": [
|
||||
"double_top"
|
||||
],
|
||||
"technical": {
|
||||
"rsi": null,
|
||||
"macd": null,
|
||||
"bollinger": null,
|
||||
"atr": null
|
||||
},
|
||||
"prediction": 0.01929405266773886,
|
||||
"swarmPredictions": [
|
||||
0.0732855217097578,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0.02318474162893649
|
||||
],
|
||||
"timeframe": "1h",
|
||||
"strategy": "ensemble"
|
||||
}
|
||||
5
examples/apify/neural-trader-system/test-analyze.json
Normal file
5
examples/apify/neural-trader-system/test-analyze.json
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"mode": "analyze",
|
||||
"symbols": ["AAPL"],
|
||||
"dataSource": "yahoo"
|
||||
}
|
||||
7
examples/apify/neural-trader-system/test-backtest.json
Normal file
7
examples/apify/neural-trader-system/test-backtest.json
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
"mode": "backtest",
|
||||
"symbols": ["AAPL"],
|
||||
"initialCapital": 100000,
|
||||
"monteCarloRuns": 100,
|
||||
"strategy": "ensemble"
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue