DeltaFlow is an enterprise-grade automated quantitative analytics platform designed to ingest high-frequency market data, model statistical distribution variations via an integrated ARIMA-GARCH forecasting framework, and route risk-adjusted structural entry signals downstream to local databases and messaging layers.
The system is engineered as an interconnected array of decoupled micro-modules, ensuring high cohesion and preventing state corruption across calculation loops:
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predictive_model.py: The mathematical core. Calculates asset log returns, runs unit-root stationarity testing, and optimizes joint$ARIMA(1, 0, 1) - GARCH(1, 1)$ architectures to generate out-of-sample forward expectation returns and instantaneous volatility fields. -
signal_generator.py: Risk-management node. Normalizes distribution output data maps using custom rolling Z-score parameters ($1.25z$ threshold barriers) to output position directives (BUY,HOLD,SELL) alongside defensive bracket margins. -
database_manager.py: A transactional storage interface that safely logs multi-column model matrices into a persistent local SQLite data warehouse (deltaflow_metrics.db) without leaving hanging file handles. -
notifier.py: Network communication client. Formats real-time analytical run snapshots into stylized markdown logs and routes them downstream via Telegram API sockets. -
run_pipeline.py: Sequential orchestrator that fits, validates, logs, and broadcasts a single end-to-end execution pass. -
worker_loop.py: An insulated automated daemon thread that synchronizes execution loops precisely with historical candle boundaries to eliminate chronological clock drift. -
dashboard_ui.py: A desktop terminal front-end written inCustomTkinterthat isolates the worker daemon thread while providing real-time data table auditing. -
test_deltaflow.py: Self-contained Quality Assurance (QA) suite executing synthetic matrix validation tests across the pipeline.
Ensure you are using Python 3.11+ (Fully verified on Python 3.14). Install the mathematical modeling packages and modern graphical window frameworks:
pip install pandas numpy arch statsmodels customtkinter requests
🚀 Execution & Operational Modes
DeltaFlow supports multiple operational workflows depending on your environment needs. Navigate into your workspace directory first:cd C:\Users\benja\Documents\Main\deltaflow- Launch the Visual Monitoring Dashboard (Recommended) Spawns a dark-mode graphical user interface to monitor the engine parameters visually. It starts the background worker loop automatically and updates tables live every 3 seconds:
python dashboard_ui.py- Run Headless Continuous Core Daemon Launches an autonomous command-line process that sleeps and self-corrects until the closing tick of the next hourly market candle:
python worker_loop.py- Run a Single Test-Trace Pass Orchestrates a single discrete data-ingestion, estimation, persistence, and messaging run sequence:
python run_pipeline.py- Execute the System Test Suite Runs the internal unit tests to validate system integrity and verify math convergence loops are performing perfectly:
python test_deltaflow.py📋 Sample Run Metric Record Output When running, the execution pipeline generates clean, highly detailed, scannable structural summary diagnostics:
2026-06-28 14:41:01 [INFO] DeltaFlow.Orchestrator: Market Data Loaded | Spot: $60,302.01 2026-06-28 14:41:01 [INFO] DeltaFlow.PredictiveModel: Stationarity metrics updated: Stationary | p-val: 0.0000 2026-06-28 14:41:01 [INFO] DeltaFlow.PredictiveModel: ARIMA optimization complete. Structural AIC: 779.73 2026-06-28 14:41:02 [INFO] DeltaFlow.PredictiveModel: GARCH optimization complete. Inst. Volatility: 0.003886 2026-06-28 14:41:02 [INFO] DeltaFlow.SignalGenerator: Signal Matrix Computed | Action: HOLD (Z-Score: -0.08) 2026-06-28 14:41:02 [INFO] DeltaFlow.Notifier: Telemetric execution alert successfully routed to Telegram client.