TradePulse

Enterprise-Grade Algorithmic Trading Platform with Geometric Market Intelligence

TradePulse is a production-grade algorithmic trading platform combining advanced geometric market indicators with enterprise reliability for quantitative researchers, algorithmic traders, and financial institutions.

Quick Start • Features • Documentation • Contributing

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📋 Table of Contents

• Why TradePulse?
• Feature Highlights
• System Architecture
• Quick Start
• Usage Examples
• TACL: Thermodynamic Control Layer
• Testing & Quality
• Performance
• Configuration
• Deployment
• Use Cases
• Project Status & Roadmap
• Contributing
• License
• Disclaimer

---

🎯 Why TradePulse?

For Quantitative Researchers

• Geometric Market Indicators: Kuramoto oscillators, Ricci flow, entropy measures for deep market analysis
• Research → Production Pipeline: Seamless transition from research to live trading
• Advanced Backtesting: Event-driven engine with walk-forward optimization and property-based testing

For Algorithmic Traders

• Multi-Exchange Support: Binance, Coinbase, Kraken, Alpaca, and more via CCXT
• Live Trading: Real-time signal generation and execution with built-in risk management
• Observability: Prometheus metrics, OpenTelemetry tracing, and comprehensive logging

For Infrastructure Engineers

• Enterprise-Grade: Security controls aligned with NIST SP 800-53 and ISO 27001 (design aligned, no external audit)
• Scalable Architecture: Event-driven design, Kubernetes-ready (GPU acceleration planned)
• Comprehensive Testing: CI pipeline spans unit, integration, property-based, and fuzz suites; coverage expansion is in progress (~71% overall) toward a 98% target on core/, execution, runtime, and tacl

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✨ Feature Highlights

🧮 Geometric Market Intelligence

Kuramoto Oscillators — Detect synchronization patterns in market dynamics
Ricci Flow — Measure geometric curvature for regime detection
Entropy Measures — Information-theoretic market analysis
Multi-Scale Analysis — Fractal pattern recognition across timeframes

Code: core/indicators/

📊 Backtesting & Simulation

Event-Driven Engine — Microsecond-latency simulation architecture
Portfolio Management — Multi-asset portfolio optimization and rebalancing
Risk Management — Pre-trade checks, position limits, drawdown protection
Walk-Forward Optimization — Defense against overfitting
Property-Based Tests — Hypothesis-driven strategy validation

Code: backtest/, execution/

⚡ Live Trading & Integration

Exchange Connectors — CCXT, Alpaca, Polygon APIs
Execution Layer — REST and WebSocket execution adapters
Runtime Layer — Live trading orchestration and monitoring
Paper Trading — Safe testing before live deployment
Kill Switch — Emergency stop with secure admin API

Code: execution/, runtime/, interfaces/live_runner.py

🛡️ Observability & Safety

Metrics — Prometheus exporters for real-time monitoring
Tracing — OpenTelemetry distributed tracing
Circuit Breakers — Auto trading halt after failures
Audit Logging — 400-day retention with compliance support
Health Checks — Kubernetes-ready liveness and readiness probes
Reliability Testing — 40+ tests validating graceful failure modes (docs/RELIABILITY_SCENARIOS.md)

Code: observability/, infra/, tests/reliability/

🔐 Enterprise Security

⚠️ No External Audit: TradePulse has not undergone external security audit or compliance certification.

Security Framework — Controls aligned with NIST SP 800-53 and ISO 27001 (design aligned, no external audit)
Secrets Management — HashiCorp Vault and AWS Secrets Manager integration
Encrypted Storage — AES-256 at rest, TLS 1.3 in transit
MFA Support — Multi-factor authentication for admin operations
Compliance Controls — GDPR, CCPA, SEC, FINRA patterns implemented (not certified)

Code: Security Documentation, SECURITY.md

🚀 Extensibility

Strategy Plugins — Easy integration of custom strategies
Custom Indicators — Add your own technical or geometric indicators
Rust Accelerators — High-performance compute kernels
Neuro Modules — Advanced neural trading components

All plugins implement the standard registry contracts in strategies/registry.py and indicator interfaces in core/indicators/base.py; RL/Neuro modules are exercised in a sandbox gate before any live test.

Code: strategies/, rust/tradepulse-accel/, hydrobrain_v2/, rl/

---

🏗️ System Architecture

graph TB
    A[📥 Data Ingestion] --> B[📦 Feature Store]
    B --> C[🧠 Strategy Engine]
    C --> D[🛡️ Risk Manager]
    D --> E[⚡ Execution Layer]
    E --> F[🌐 Exchange APIs]
    C --> G[📊 Analytics]
    G --> H[📡 Observability]
    
    style A fill:#4a90e2,stroke:#357abd,color:#fff
    style B fill:#9b59b6,stroke:#7d3c98,color:#fff
    style C fill:#7ed321,stroke:#5fa319,color:#fff
    style D fill:#e74c3c,stroke:#c0392b,color:#fff
    style E fill:#f5a623,stroke:#c47d1a,color:#fff
    style F fill:#1abc9c,stroke:#16a085,color:#fff
    style G fill:#3498db,stroke:#2980b9,color:#fff
    style H fill:#bd10e0,stroke:#8e0ca8,color:#fff

Loading

Key Modules

Module	Path	Language	Purpose
Core Indicators	core/indicators/	Python	Geometric and technical indicators
Backtest Engine	backtest/	Python	Event-driven backtesting
Execution	execution/	Python	Order execution and adapters
Runtime	runtime/	Python	Live trading orchestration
Observability	observability/	Python	Metrics, traces, dashboards
UI Dashboard	ui/dashboard/	TypeScript	Interactive web interface
Rust Accelerators	rust/tradepulse-accel/	Rust	High-performance compute
HydroBrain v2	hydrobrain_v2/	Python	Advanced neural components
RL Module	rl/	Python	Reinforcement learning strategies
TACL	tacl/	Python	Thermodynamic control layer

📖 Full Architecture: docs/ARCHITECTURE.md 📦 Independent Models Split: docs/architecture/independent_models.md

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🚀 Quick Start

Prerequisites

• Python 3.11 or 3.12 (Download)
• Git 2.30+ for version control
• Docker (optional, for containerized deployment)

Installation

# Clone the repository
git clone https://github.com/neuron7x/TradePulse.git
cd TradePulse

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies (choose one):
make install           # Runtime dependencies only
make dev-install       # Full development environment

# Configure environment variables
cp .env.example .env
# Edit .env with your settings (see SETUP.md for details)

Alternative installation (without Make):

pip install --upgrade pip setuptools wheel
pip install -c constraints/security.txt -r requirements.lock          # Runtime only
pip install -c constraints/security.txt -r requirements-dev.lock      # Add dev tools

📖 Detailed Setup: SETUP.md
🔐 Security: All dependencies are pinned to exact versions with security constraints applied

Your First Analysis

# Run the quick start example
PYTHONPATH=. python examples/quick_start.py

Expected output:

=== TradePulse Market Analysis ===
----------------------------------------
Market Phase:     transition
Confidence:       0.893
Entry Signal:     0.000
----------------------------------------

📊 Interpretation:
  • Market is transitioning between regimes
  • High confidence (89.3%) in current phase

✅ Analysis complete!

Note: Use PYTHONPATH=. to ensure Python can find the local modules. On Windows PowerShell: $env:PYTHONPATH='.'; python examples/quick_start.py

Run the control platform (canonical)

tradepulse-server --allow-plaintext --host 127.0.0.1 --port 8000

• Optional: --config path/to/config.yaml (precedence: CLI > ENV > YAML > defaults)
• Logs include effective_config_source=... controllers_loaded=[...]
• Fallback (non-canonical): PYTHONPATH=. python -m application.runtime.server --allow-plaintext --host 127.0.0.1 --port 8000

Canonical code root

• Package root: tradepulse under src/
• Import controllers from canonical paths, e.g.:

  from tradepulse.core.neuro.serotonin.serotonin_controller import SerotoninController

• Legacy core.* imports are deprecated shims and will be removed in a future release; see docs/ARCHITECTURE_MAP.md.

Interactive Dashboard

Canonical — TypeScript dashboard (ui/dashboard)

cd ui/dashboard
npm ci
npm test  # fast, headless smoke that exercises the canonical UI

Optional preview: open demo.html in a local HTTP server for visual inspection.

Prototype — Streamlit dashboard (dev-only)

PYTHONPATH=. streamlit run interfaces/dashboard_streamlit.py

Prototype only; keep Streamlit installed separately (pip install streamlit streamlit-authenticator).

📖 Dashboard Guide: docs/ui_logical_structure.md

Golden Path Workflow

Demonstrate the complete TradePulse workflow from data to results:

# One-command demonstration of the full workflow
make golden-path

What it does:

1. Data Generation — Creates 500 bars of synthetic market data with deterministic seed
2. Market Analysis — Detects regime using Kuramoto-Ricci indicators
3. Backtest Integration — Validates strategy execution with PnL calculation
4. Results — Produces validated output in <30 seconds

Expected output:

🎯 TradePulse Golden Path Workflow
====================================
Step 1/3: Generating synthetic market data...
✓ Generated 500 bars of synthetic data

Step 2/3: Running market analysis...
=== TradePulse Market Analysis ===
Market Phase:     transition
Confidence:       0.658
Entry Signal:     0.000

Step 3/3: Running backtest integration test...
tests/integration/test_golden_path_backtest.py . [100%]
1 passed in 0.84s

✅ Golden Path Complete!

Note: This is a demonstration workflow using synthetic data. For production use with real market data, see DEPLOYMENT.md and docs/runbook_live_trading.md.

---

💻 Usage Examples

Basic Market Analysis

import numpy as np
import pandas as pd
from core.indicators.kuramoto_ricci_composite import TradePulseCompositeEngine

# Generate sample market data
index = pd.date_range("2024-01-01", periods=720, freq="5min")
prices = 100 + np.cumsum(np.random.normal(0, 0.6, 720))
volume = np.random.lognormal(9.5, 0.35, 720)
bars = pd.DataFrame({"close": prices, "volume": volume}, index=index)

# Analyze market regime
engine = TradePulseCompositeEngine()
snapshot = engine.analyze_market(bars)

print(f"📊 Phase: {snapshot.phase.value}")
print(f"🎯 Confidence: {snapshot.confidence:.3f}")
print(f"📈 Entry Signal: {snapshot.entry_signal:.3f}")

Backtesting a Strategy

import numpy as np
from backtest.event_driven import EventDrivenBacktestEngine
from core.indicators import KuramotoIndicator

# Generate price series
rng = np.random.default_rng(seed=42)
prices = 100 + np.cumsum(rng.normal(0, 1, 500))

# Define indicator and signal function
indicator = KuramotoIndicator(window=80, coupling=0.9)

def kuramoto_signal(series: np.ndarray) -> np.ndarray:
    order = indicator.compute(series)
    signal = np.where(order > 0.75, 1.0, np.where(order < 0.25, -1.0, 0.0))
    warmup = min(indicator.window, signal.size)
    signal[:warmup] = 0.0
    return signal

# Run backtest
engine = EventDrivenBacktestEngine()
result = engine.run(
    prices,
    kuramoto_signal,
    initial_capital=100_000,
    strategy_name="kuramoto_demo",
)

print(f"💰 PnL: ${result.pnl:,.2f}")
print(f"📉 Max Drawdown: {result.max_dd:.2%}")
print(f"📊 Trades: {result.trades}")

CLI Analysis

# Analyze CSV data
python -m interfaces.cli analyze \
    --csv data/sample.csv \
    --window 200 \
    --price-col close

# Generate sample data
python -m interfaces.cli generate \
    --output data/synthetic.csv \
    --bars 1000

📖 CLI Reference: docs/tradepulse_cli_reference.md

---

🌡️ TACL: Thermodynamic Autonomic Control Layer

TACL is the governing brain of system stability, not a feature. It manages the TradePulse topology as a thermodynamic system where autonomous changes must respect Monotonic Free Energy Descent: no self-degrading change without explicit human approval.

Global Invariants

TACL enforces five non-negotiable invariants across all autonomous operations:

Invariant	Description	Failure Consequence
Safety	No uncontrolled degradation; explicit human approval for non-monotonic moves	Automated rollback triggered
Auditability	7-year retention for decisions, config, model/policy versions, regime switches	Compliance violation, incident response blocked
Resource Governance	Latency, coherency, and resource cost are first-class (Free Energy F)	SLO breach, capacity exhaustion
Reproducibility	Deterministic replay; versioned configs; testable interfaces	Debug/audit impossible
Event-based Sparsity	Compute is sparse and triggered; avoid always-on overhead	Resource waste, latency degradation

Core Mechanisms

Free Energy Measurement

Free Energy F = U - T·S where:

• U (Internal Energy): Weighted penalties from latency, coherency, and resource metrics
• T (Temperature): Control temperature (0.60) representing discount on available slack
• S (Entropy): Stability term proportional to headroom relative to thresholds

Metric	Threshold	Weight	Unit
latency_p95	85.0	1.6	ms
latency_p99	120.0	1.9	ms
coherency_drift	0.08	1.2	ratio
cpu_burn	0.75	0.9	ratio
mem_cost	6.5	0.8	GiB
queue_depth	32.0	0.7	messages
packet_loss	0.005	1.4	ratio

Energy Envelope: Free energy ≤ 1.35 (12% safety margin from hot-path load tests)

Telemetry Discipline: Latency, coherency, and cost signals are exported as OTLP traces and Prometheus time-series. Every controller state transition is logged for ≥400-day retention. RL/GA/LinkActivator actuators remain locked behind dual human approval plus CI safety gates—no autonomous refactors are permitted without the gate.

Safety Model: Monotonic Free Energy Descent

IF F(t+1) > F(t) + tolerance THEN
  IF has_dual_approval(operations, safety) THEN
    log_override_with_approvals()
    proceed()
  ELSE
    trigger_automated_rollback()
    engage_kill_switch_authority()
  END
END

• Rest Potential: 1.0 (stabilised baseline)
• Action Potential: 1.35 (maximum tolerable stress before kill-switch)
• Monotonic Tolerance: 5×10⁻³

Protocol Hot-Swap

Dynamic switching between communication protocols with admissibility guards:

• RDMA: Low-latency, high-throughput (requires compatible hardware)
• CRDT: Conflict-free replicated data types for eventual consistency
• gRPC: Standard RPC with protobuf serialization
• Shared Memory: Ultra-low latency for co-located services
• Gossip: Epidemic protocol for distributed state synchronization

Rollback Policy: Any hot-swap that increases F beyond tolerance triggers automatic reversion within 30s.

Technical Classification

Aspect	Status	Verification
TRL	7 (internal assessment, post-staging)	Staging load tests
Adaptation	GA/RL with runtime monotonic gates	CI safety gates
Audit Trail	7-year retention (config present, production validation pending)	.ci_artifacts/energy_validation.json
Crisis Modes	Adaptive recovery with severity escalation	runtime/thermo_controller.py

Authorization for Exceptions

Temporary exceptions to the energy budget require dual approval:

1. Thermodynamic Duty Officer (rotating weekly)
2. Platform Staff Engineer (responsible for affected cluster)

Both approvals must be recorded in the release ticket with telemetry snapshots.

📖 TACL Documentation: docs/TACL.md, tacl/, runtime/thermo_controller.py

📊 Metrics Formalization: docs/thermodynamics/METRICS_FORMALIZATION.md

⚙️ Operational Runbook: docs/thermodynamics/OPERATIONAL_RUNBOOK.md

---

🧪 Testing & Quality

📊 Claims Registry: All high-level quality, performance, and security claims are tracked in docs/METRICS_CONTRACT.md.

TradePulse maintains multi-layer testing. Current coverage is ~71% (see docs/project-status.md); CI is configured for a 98% critical-surface target that is still being activated while coverage expands. The testing strategies include:

Test Types

• Unit Tests: Module-level validation (tests/unit/)
• Integration Tests: End-to-end workflows (tests/integration/)
• Property-Based Tests: Hypothesis-driven testing (tests/property/)
• Fuzz Tests: Adversarial input testing (tests/fuzz/)
• Contract Tests: API schema validation (tests/contracts/)
• Mutation Testing: Test suite quality assurance

Running Tests

# Run all tests
pytest tests/

# Fast feedback loop (skip slow tests)
pytest tests/ -m "not slow"

# With coverage report
pytest tests/ --cov=core --cov=backtest --cov=execution --cov-report=html

# Property-based tests only
pytest tests/property/

# Mutation testing
mutmut run --use-coverage

# UI dashboard smoke (canonical)
cd ui/dashboard && npm test

CI/CD Merge Gates

• Test coverage: CI configurations run pytest -m "not flaky" across unit, integration, property-based, fuzz, contracts, reliability, and e2e smoke suites. The critical-surface coverage target is 98%, but the enforcement gate is not yet active (current overall coverage ~71%).
• Security & Quality: Required workflows execute SAST (ruff, mypy, bandit, golangci-lint), dependency checks (pip-audit with constraints/security.txt), and SBOM generation/verification (CycloneDX) before merge.
• Artifacts: Coverage XML, mutation reports, and SBOMs are uploaded per run to document evidence for internal audits.

Coverage Status

Target Gate: CI configuration sets a 98% line coverage goal on core/, execution/, runtime/, and tacl/ using the critical-surface guardrail (configs/quality/critical_surface.coveragerc + configs/quality/critical_surface.toml), but enforcement is pending while coverage expands. Current snapshot: ~71% overall coverage (backtest ~74%, execution ~44%, core ~32%).
Module Goals: backtest (100%), execution (100%), core modules (90-95%) with branch coverage parity.

To verify current coverage:

make test-coverage
# View report: reports/coverage/index.html

📊 Full claims mapping: docs/METRICS_CONTRACT.md

---

⚡ Performance

TradePulse is designed for low-latency, high-throughput trading operations.

⚠️ Design Targets Only: Performance metrics below are architecture targets (status: design_target), NOT measured results.

Instrumentation captures latency, memory, and compute budgets for data ingestion, execution, runtime orchestration, and dashboard queries via Prometheus/OpenTelemetry. Any code path consuming >30% of the hot-loop budget must ship (or fall back to) a Rust accelerator from rust/tradepulse-accel/.

Design Goals

All claims below have status design_target. See docs/METRICS_CONTRACT.md for definitions.

• Backtesting: 1M+ bars/second throughput
• Live Trading: Sub-5ms order latency (exchange dependent)
• Signal Generation: Sub-1ms with cached indicators
• Memory: ~200MB steady-state for live trading
• GPU Acceleration: planned — CUDA kernels not implemented

Benchmarks

# Run performance benchmarks
make perf

# Run specific benchmark tests
pytest tests/performance/test_indicator_benchmarks.py --benchmark-enable

📊 Full claims mapping: docs/METRICS_CONTRACT.md

---

⚙️ Configuration

TradePulse uses Hydra for flexible, composable configuration management.

📜 Environment Charter: Deterministic dependency/config/data rules live in docs/ENVIRONMENT_CHARTER.md.

Configuration Structure

TradePulse uses three configuration directories, each with a specific purpose:

• conf/ — Hydra framework configs and experiment settings
• config/ — Core neuromodulator and thermodynamic system configs
• configs/ — Application and service-level configurations
• envs/ — Environment-specific settings
• .env — Environment variables (not committed)

For detailed information about each directory's purpose and usage, see Configuration Structure Guide.

Example Configuration

# config.yaml
strategy:
  name: momentum
  capital: 100000

data:
  source: binance
  symbols: [BTC/USDT, ETH/USDT]
  timeframe: 1h

execution:
  slippage: 0.001
  commission: 0.001

risk:
  max_position_pct: 0.2
  stop_loss_pct: 0.02

Command-Line Overrides

# Override configuration from command line
tradepulse run strategy.capital=200000 data.timeframe=4h

📖 Configuration Guide: docs/configuration.md

---

🚀 Deployment

Docker Compose (Development & Staging)

# Configure environment
cp .env.example .env
# Edit .env with your secrets

# Start services
docker compose up -d

# Check status
docker compose ps

# View logs
docker compose logs -f tradepulse

Kubernetes (Production)

# Provision EKS cluster (Terraform)
terraform -chdir=infra/terraform/eks init
terraform -chdir=infra/terraform/eks workspace select production
terraform -chdir=infra/terraform/eks apply -var-file=environments/production.tfvars

# Deploy with Kustomize
kubectl apply -k deploy/kustomize/overlays/production
kubectl rollout status deployment/tradepulse-api -n tradepulse-production

📖 Deployment Guide: DEPLOYMENT.md

---

🎯 Use Cases

Quantitative Researcher

# Multi-scale indicator composition
from tradepulse.indicators import MultiscaleKuramoto

indicator = MultiscaleKuramoto(
    scales=[5, 15, 60],  # Multi-timeframe analysis
    coupling=0.7,
)

# Automatic feature versioning
signals = indicator.compute(data, version="v2.1.0")

Use for: Strategy development, indicator research, pattern discovery, hypothesis testing

Algorithmic Trader

# Real-time signal generation
from tradepulse.live import LiveTrader

trader = LiveTrader(
    strategy=your_strategy,
    exchange="binance",
    mode="paper",  # Safe testing before live
)
trader.start()

Use for: Fast execution, real-time signals, live trading, mobile alerts

Infrastructure Engineer

# Enterprise risk management
from tradepulse.risk import RiskManager

risk_manager = RiskManager(
    max_position_size=100_000,
    max_leverage=3.0,
    stop_loss_pct=0.02,
    compliance_checks=["mifid2", "position_limits"],
)

Use for: Compliance, portfolio management, risk control, multi-strategy orchestration

---

📈 Project Status & Roadmap

Current Version: v0.1.0

Status: Pre-Production Beta — Core functionality stable, preparing for v1.0 release

Current Focus (December 2025): Test coverage expansion, documentation finalization, dashboard hardening

Component Maturity

Component	Status	Stability
Core Engine	✅ Production Ready	Stable
Indicators (50+)	✅ Production Ready	Stable
Backtesting	✅ Production Ready	Stable
Live Trading	🔄 Beta	Active Development
Web Dashboard	🚧 Alpha	Early Preview
Documentation	🔄 In Progress	85% Complete

Development Roadmap

• Q4 2025 → Q1 2026: Complete live trading module, finalize dashboard
• Q1 2026: v1.0 production release preparation
• Q2 2026: Options & derivatives support
• Q3-Q4 2026: Multi-asset portfolio optimization and advanced features

📖 Full Roadmap: docs/roadmap.md
📰 Changelog: CHANGELOG.md
📋 Product Planning: PRODUCT_PAIN_SOLUTION.md

---

🤝 Contributing

We welcome contributions! Whether bug fixes, new features, or documentation improvements — every contribution matters.

Developer Workflow & Automation

TradePulse provides standardized make commands for all development tasks:

# Setup development environment
git clone https://github.com/neuron7x/TradePulse.git
cd TradePulse
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Core commands
make install       # Install all dependencies
make test          # Run core test suite (fast, CI-safe)
make lint          # Run all linters (Python + Go + shell)
make format        # Auto-format code (black, isort, ruff)
make audit         # Run security audits (bandit, pip-audit)
make clean         # Remove cache and build artifacts

# Extended commands
make test-all      # Full test suite with coverage
make test-fast     # Fast unit tests only
make perf          # Performance benchmarks
make e2e           # End-to-end smoke tests
make docs          # Build documentation
make release       # Local release build helper

# View all available commands
make help

CI/CD Pipelines:

• 🧪 tests.yml — Comprehensive test suite for all PRs (lint, type-check, unit, integration, property tests)
• 📊 ci.yml — Coverage tracking and mutation testing on main branch
• 🔒 security-policy-enforcement.yml — Security scans for PRs
• 🚀 publish-python.yml / publish-image.yml — Release automation on tagged versions

See CONTRIBUTING.md for detailed development guidelines.

First-Time Contributors

1. Browse good first issues
2. Read the Contributing Guide
3. Join our GitHub Discussions
4. Submit your first PR!

📖 Contributing Guide: CONTRIBUTING.md
🤝 Code of Conduct: CODE_OF_CONDUCT.md

---

📚 Documentation

Getting Started

• ⚙️ Environment Setup — Development environment guide
• 🚀 Quickstart Guide — Step-by-step tutorials
• 🖥️ User Interaction Guide — CLI, Dashboard, API

Technical Documentation

• 🏗️ Architecture Overview — System design deep dive
• 📡 API Reference — Complete API documentation
• 📊 Indicator Library — Available indicators and usage
• 🚀 Deployment Guide — Production rollouts
• 🧭 Requirements Traceability Matrix — Requirement-to-implementation mapping

Security & Operations

• 🔐 Security Framework — Comprehensive security documentation
• 🛡️ Security Policy — Vulnerability reporting
• ⚙️ Operational Artifacts — Production ops guide
• 📋 Incident Playbooks — Response procedures

📖 Full Documentation: docs/

---

🌐 Community & Support

Documentation

• 📖 User Guide
• 📡 API Reference
• ❓ FAQ
• 🔧 Troubleshooting

Community

• 💬 GitHub Discussions — Q&A, ideas, show & tell
• 📚 Stack Overflow — Tagged questions

Issues

• 🐛 Bug Reports
• ✨ Feature Requests
• 🔒 Security Issues — Private vulnerability reporting

---

📜 License

TradePulse is distributed under the TradePulse Proprietary License Agreement (TPLA).

The TPLA permits internal, non-commercial evaluation and development use only. Commercial usage requires a separate written agreement.

📄 Full License: LICENSE

---

⚠️ Disclaimer

⚠️ Trading involves substantial risk of loss and is not suitable for everyone.

TradePulse is an R&D/research platform provided for educational and research purposes only.

• No Performance Guarantees: This is a research laboratory, not a production trading system with guaranteed returns
• Beta Software: Live trading components are in beta; paper trading recommended for evaluation
• Due Diligence Required: Always perform your own analysis and testing before deploying strategies
• Risk Management: Never invest more than you can afford to lose

Past performance does not guarantee future results. Test strategies thoroughly in paper trading before risking real capital.

Trade responsibly. This is research software, not investment advice.

---

🙏 Acknowledgments

TradePulse is built with love using open source technology:

Core Stack: Python, NumPy, pandas, FastAPI, Streamlit
Analytics: SciPy, scikit-learn, PyTorch, Numba
Infrastructure: Docker, Kubernetes, Prometheus, Redis
Inspired by: Zipline, Backtrader, QuantLib

Special thanks to all contributors who have helped build TradePulse!

---

Made with ❤️ by the TradePulse Community

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