v0.5.0-beta — The DePIN & Trade-to-Mine Genesis 💎🐝

🚀 DepthSight v0.5.0-beta: The First Algorithmic Trading DePIN

We are thrilled to announce DepthSight v0.5.0-beta — the largest architectural milestone in the project's history (+37,000 LOC diff).
This release transforms DepthSight from a traditional self-hosted SaaS into a Decentralized Physical Infrastructure Network (DePIN) for algorithmic trading, introducing our native Proof-of-Trade Mining ($DEPTH) economic model.

💎 1. Proof-of-Trade Mining & Positive-EV Trading

Traditional trading platforms charge monthly subscriptions while exchanges pocket 100% of trading fees. DepthSight flips this model:

• Volume-Based Mining: Your daily trading volume generates $DEPTH utility tokens backed directly by real broker rebates.
• Positive Expected Value (EV): By transforming trading fees into liquid token equity, algorithmic trading mathematically shifts in favor of the trader — even near break-even strategies become profitable through fee mining.
• Deflationary Flywheel:
  • 30% Fiat Buy-Back: 30% of platform net broker rebates are used to continuously market-buy and burn $DEPTH.
  • 70% AI Burn: 70% of tokens spent on AI Swarm intelligence queries are burned permanently.

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🌐 2. Decentralized Node Economy & Commission Control

Anyone can deploy a DepthSight node and operate as an independent trading hub:

• Custom Commission & Rebate Sharing: Node operators configure their own rebate-sharing parameters, competing globally for trading volume and liquidity.
• Federated Node Registry: Nodes optionally connect to the Federation Hub to show live heartbeat topology, share strategies, and participate in network-wide metrics with strict IP-privacy guarantees.

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🔑 3. Web3 Decentralized Identity & Cross-Server Migration

Traders are no longer locked into a single centralized server or database:

• Non-Custodial Wallet Linking: Seamlessly connect MetaMask, Phantom, or Web3 wallets.
• Cross-Node Portability: Your reputation, referral network, and mining history are tied to your cryptographic signature. You can seamlessly migrate between different DepthSight host servers without losing your historical state or relying on centralized passwords.

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⚡ 4. Core Engine, Exchanges & Infrastructure Upgrades

• Exchange Expansion: Native, fully tested support for Binance, Bybit, OKX, and WEEX with verified order book snapshots, trade streams, and execution safety.
• Testing & Stability: Over 1,500+ unit and integration tests passing.
• Async & Deadlock Fixes: Resolved aiosqlite thread deadlocks during testing teardown (dispose_test_engine), optimized Redis Pub/Sub state channels, and improved Celery task lifecycle resilience.
• Database Schema Evolution: Added full suite of Alembic migrations for payouts, mining rewards, verification statuses, and decentralized system settings.

v0.4.0-beta: AI Autopilot, MCP Memory & Slack Agent Integration

🚀 What's New in v0.4.0-beta

This major release introduces a full suite of autonomous AI capabilities, completely transforming how trading strategies are built, tested, and deployed on DepthSight.

🤖 1. AI Autopilot Agent

• Autonomous Optimization Loop: A self-correcting cycle (Generate -> Backtest -> Learn -> Repeat) that builds and refines trading strategies automatically.
• Multi-Agent Architecture: Built-in specialized roles (Generator, Critic, and Strategy Advisor) that work together to analyze results and suggest improvements.
• Visual Chart Analysis: On the frontend, the agent can "see" chart screenshots to recognize patterns and build strategies based on visual context.

🧠 2. MCP (Model Context Protocol) Memory Server

• Semantic Memory Retrieval: The AI now remembers past backtests, successful parameter combinations, and historical mistakes.
• Dynamic Rules Context: Automatically feeds the AI with global trading guidelines and user-specific insights to prevent overfitting and over-filtering.
• Continuous Learning: Every optimization loop adds new data to the vector database, making the agent smarter over time.

💬 3. Slack Agent Integration (/depthsight)

• Full Platform Control via Slack: Run backtests, optimize strategies, and interact with the AI directly from your workspace.
• Real-time Performance Cards: Beautifully rendered backtest reports with equity curves, KPIs, and PnL metrics delivered straight to your chat.
• Interactive Workflows: Added interactive buttons after successful optimization to instantly Open in Editor or Deploy Live Bot.
• Context Management: Added /depthsight clear command to easily reset the agent's context and prevent hallucination loops.

🛠️ Improvements & Bug Fixes

• Fixed an issue with silent failures during the rendering of final Autopilot report cards.
• Refactored backtest polling for long-running Celery tasks to ensure stable delivery of results.
• Optimized the strictness of the AI Strategy Advisor to prevent generating strategies with overly restrictive confluence (the "2 trades only" issue).

DepthSight Open Beta 0.3

Release Notes v0.3.0-beta

This release focuses on a deep architectural refactoring of the API and Celery integration layers, stabilizing the test suite by eliminating flaky test behaviors, and preparing the repository for continuous integration (CI/CD) with code coverage tracking.

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Architectural Changes and Refactoring

• Circular Dependency Elimination: Celery/Redis configuration and environment variable loading have been decoupled from the API core and moved into a dedicated infrastructure layer (infra/config.py and infra/celery_app.py). This fully resolves the api.celery_app -> bot_module.config dependency cycle.
• Streamlined API Routing: Replaced the legacy ModuleProxy(lambda: depthsight_api...) implementation in endpoint definitions. Requests are now routed directly through explicit route submodules (api.routes.backtests and api.routes.diagnostics).
• Backward Compatibility: Retained a compatibility import layer to ensure existing tests and third-party integrations do not break.

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Test Suite and CI/CD Stability

• Pandas 3.0 Compatibility: Resolved chained assignment warnings/errors (caused by inplace=True operations on Series copies) that left the ATR column populated with NaNs under new Pandas versions in the CI environment.
• Deterministic Strategy Builder Tests: Converted test_limit_retest_order and test_limit_break_signal_mode_and_entry_price from random walk candles to fully deterministic layouts with explicit stop-loss triggering to guarantee reliable local and remote runs.
• Isolated ML Model Tests: Added a module-level skip check to test_oracle_integration.py that bypasses the oracle test suite when the heavy ML weights file data/oracle_model.joblib is not checked into the repository (typical for headless CI runners).
• CI Environment Mocks: Configured mock values for environment-level secrets (JWT_SECRET_KEY, API_ENCRYPTION_KEY, API_KEY_SECRET) in conftest.py to prevent FastAPI bootstrap crashes during isolated pipeline stages.
• Isolated Test Strategy Registration: Registered VolumeBreakout and FakeBreakout strategies dynamically within the portfolio backtester test suite to keep test environments decoupled from production registry constraints.

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Dependency and Coverage Management

• CI Python Runtime Upgrade: Upgraded the setup-python action version to 3.12 to mirror the production Docker container and fix package compilation issues on Python 3.13.
• Locked Dependencies: Pinned critical package versions (ccxt==4.4.89, redis==6.2.0, fakeredis==2.30.0) in requirements.txt to eliminate behavioral differences between development machines and Actions runners.
• Codecov Integration: Configured XML test coverage reporting (pytest-cov) and integrated the official Codecov GitHub Action to track code coverage stats on pull requests and commits.

DepthSight Open Beta Release

Welcome to the first public Open Beta release of DepthSight!

DepthSight is an Enterprise-Grade, Multi-Tenant SaaS-in-a-Box for Algorithmic Trading[cite: 2]. This release marks the transition from closed development to an open-source (AGPL-3.0) community hub[cite: 2].

Key Features in v0.1.0-beta

• Visual Strategy Builder: Drag-and-drop node interface with 40+ logic blocks (Order Book, Tape, Volume Delta, etc.).
• AI Co-pilot: Generate complete strategy node trees from plain English prompts using Structured Outputs.
• Federated Discovery Hub: Securely share and import backtested strategies within the community.
• Market Data Fan-out: Centralized WebSocket manager using Redis Pub/Sub to bypass exchange rate limits.
• 1-Click Deployment: Fully Dockerized setup with an automated deploy.sh script for Ubuntu VPS environments.

Supported Exchanges

Native integration currently supports Binance, Bybit, Bitget, OKX, Gate.io, and BingX[cite: 2].

Important Notice (Open Beta)

This software is provided "as is". Algorithmic trading involves real financial risk[cite: 2].
Please use testnet or paper trading first[cite: 2]. Do not connect real funds unless you fully understand the code and have verified the entire workflow in a controlled environment[cite: 2].

Get Involved

If you encounter any bugs in the deployment script, node execution, or UI, please open an Issue. Pull Requests for new ccxt exchange connectors or custom logic nodes are highly welcome!