ChronoBridge — Multi‑Modal Data Synchronization & Embedding Service

ChronoBridge is a data synchronization and representation service designed to extract, align, and distribute time‑coherent multimodal embeddings per symbol from heterogeneous data sources.
It serves as the data backbone for downstream monitoring, structural analysis, and risk‑aware intelligence systems.

ChronoBridge is a data and representation layer only.
It does not generate trading signals, forecasts, recommendations, allocations, or execution instructions.
All outputs are descriptive representations intended for monitoring, diagnostics, and research workflows.

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Overview

The ChronoBridge Service extracts synchronized, fused embeddings per symbol from a trained NeuralFusionCore model and persists them to MongoDB and Redis for fast downstream access.

ChronoBridge integrates:

• OHLCV market data
• Time‑aligned news embeddings

and produces temporally consistent representations that ensure all downstream systems observe the same market context at the same time.

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Key Capabilities

• Multi‑source data ingestion and temporal alignment
• Feature construction for multimodal inference
• Extraction of fused embeddings from a trained NeuralFusionCore
• Sliding‑window inference for continuous context tracking
• Low‑latency serving via MongoDB and Redis

ChronoBridge focuses on data quality, consistency, and synchronization, not decision‑making.

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Architecture (Logical Flow)

1. Data Ingest Service
   Pulls recent OHLCV and news data for a specified rolling window.

2. Feature Service
   Builds price‑based features and aligns news embeddings to the unified time grid.

3. NeuralFusionCore Inference
   Generates fused multimodal embeddings per symbol.

4. Persistence Layer
   Stores embeddings and metadata in MongoDB and Redis.

5. Sliding Window Execution
   Ensures historical context is preserved for each inference step.

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Repository Layout

ChronoBridge/
├── scripts/
│   ├── data_ingest_service.py
│   ├── features_service.py
│   ├── chronobridge_api_service.py
│   └── chronobridge_service.py
├── data/
│   └── processed/
├── src/
│   └── inference.py
├── lib/
│   ├── features.py
│   ├── market.py
│   ├── news.py
│   ├── utils.py
│   └── redis_utils.py
├── models/
├── apps/
│   └── NeuralFusionCore/
│       ├── data/
│       ├── lib/
│       ├── scripts/
│       ├── config.py
│       └── README.md
└── README.md

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Setup

Dependencies

• Python 3.11+
• PyTorch 2.x
• MongoDB (pymongo)
• Redis (redis)
• NeuralFusionCore module (apps.NeuralFusionCore)

Install dependencies:

git clone https://github.com/Novoxpert/ChronoBridge.git
cd ChronoBridge

python -m venv .venv
source .venv/bin/activate   # Linux/macOS
# or
.\.venv\Scripts\Activate.ps1  # Windows

pip install -r requirements.txt

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Usage

Run ChronoBridge for the most recent N hours:

python chronobridge_service.py --mode synchronize --hours 4 --device cpu

Bridge mode for downstream graph and monitoring services:

python chronobridge_service.py --mode bridge --hours 10 --device cpu

Run API service:

python chronobridge_api_service.py

Arguments

• --hours : Number of past hours to process (default: 4)
• --device: cpu or cuda

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Workflow Summary

• Fetch OHLCV and news for the selected window
• Build aligned feature tensors
• Load trained NeuralFusionCore weights
• Perform sliding‑window inference
• Persist fused embeddings and metadata
• Serve results via API and cache

All steps are deterministic and reproducible for auditability.

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Outputs

MongoDB Collection

chrono_bridge

Stores per‑symbol records including:

• timestamp
• symbol
• fused embedding vector
• aligned OHLCV features

Redis Cache

Provides fast access to the latest synchronized embeddings.

These outputs are intermediate representations, not signals or recommendations.

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Notes

• NeuralFusionCore must be trained before running ChronoBridge.
• Sliding‑window inference ensures temporal continuity.
• Designed for real‑time or batch monitoring and research pipelines.

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Script Cheat‑Sheet

• scripts/data_ingest_service.py — fetches OHLCV and news, pushes to Redis
• scripts/features_service.py — builds multimodal features
• src/inference.py — extracts fused embeddings from NeuralFusionCore
• scripts/chronobridge_api_service.py — serves embeddings via API

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Authors & Citation

Developed by the Novoxpert Research Team.

If you use or extend this repository, please cite:

Novoxpert Research (2025). ChronoBridge: Multi‑Modal Data Synchronization & Embedding Service.
GitHub: https://github.com/Novoxpert/ChronoBridge

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Support

• Issues & Bugs: https://github.com/Novoxpert/ChronoBridge/issues
• Discussions: https://github.com/Novoxpert/ChronoBridge/discussions