The complete architecture, roadmap, implementation rules, agent responsibilities, UI design, mathematical modeling, and phase-wise development scope are maintained in the uploaded roadmap document:
roadmap_docx.docx
or
delta_os.docx
A probabilistic market intelligence, structural geometry, liquidity reasoning, and execution-quality platform.
DELTA OS — Intelligence Before Execution.
Most trading tools are built around one simple question:
“Should I buy or sell?”
DELTA OS is built around a better question:
“What is the market actually building, where is liquidity sitting, what is the probability of expansion, and is execution worth the risk?”
Markets do not move only because an indicator crosses another indicator.
They move because of:
- liquidity pressure
- trapped participants
- stop clusters
- volatility compression
- structural geometry
- fake breakouts
- higher-timeframe context
- lower-timeframe execution
- institutional positioning
- order flow
- risk and execution quality
DELTA OS is designed to observe those layers together.
It is not a retail bot.
It is not a signal machine.
It is not a toy AI trading system.
It is a market intelligence operating system.
DELTA OS stands for:
Dynamic Execution, Liquidity & Trading Analytics
It is designed as a professional-grade trading intelligence platform that scans markets, detects structural setups, reasons about liquidity, estimates probabilities, evaluates risk, and presents everything through a clean desktop dashboard.
The goal is not to blindly predict the next candle.
The goal is to help the trader think before execution.
DELTA OS is built to answer questions like:
- Which stocks are compressing?
- Which structures are becoming mature?
- Which breakouts are likely fake?
- Where is liquidity sitting?
- Where are stop clusters?
- Which timeframe supports the setup?
- Which timeframe invalidates it?
- Is execution quality good enough?
- Should I wait, prepare, avoid, or execute?
The complete architecture, roadmap, implementation rules, agent responsibilities, UI design, mathematical modeling, and phase-wise development scope are maintained in the uploaded roadmap document:
roadmap_docx.docx
or
delta_os.docx
This README is intentionally written as a clean project story and repository overview.
For full implementation depth, refer to the roadmap document.
The roadmap document is the source of truth for:
- product vision
- Phase-1 offline-first scope
- engineering standards
- Clean Architecture design
- multi-agent architecture
- multi-timeframe intelligence
- structural geometry engine
- liquidity concepts engine
- market microstructure layer
- probability engine
- risk engine
- PySide6 dashboard design
- voice assistant roadmap
- future broker/API/WebSocket/RL/C++ extension points
DELTA OS is not:
- a retail trading bot
- an RSI/MACD strategy
- a moving-average crossover system
- a generic LSTM price predictor
- a random reinforcement learning demo
- a blind buy/sell generator
- a monolithic Python script
- a toy dashboard
DELTA OS is designed as an institutional-style research and intelligence platform.
Markets are treated as:
- probabilistic
- hierarchical
- liquidity-driven
- regime-dependent
- path-dependent
- non-stationary
- multi-scale
- fractal
- participant-driven systems
Price movement is treated as the result of:
- liquidity pressure
- trapped participants
- volatility compression and expansion
- structural geometry
- order flow
- stop clusters
- market-maker behavior
- inventory pressure
- dealer hedging
- execution mechanics
- regime transitions
DELTA OS does not simply ask:
“Will price go up?”
It asks:
“What is the market structure, where is liquidity, what is the probability, what is the risk, and is execution valid?”
DELTA OS combines multiple layers of market reasoning.
DELTA OS treats each timeframe as a different market observer.
Supported intelligence layers include:
- Monthly
- Weekly
- Daily
- 4H
- 1H
- 15m
- 5m execution timeframe
- future 1m microstructure layer
Higher timeframes provide:
- macro structure
- institutional positioning
- long-term liquidity
- accumulation/distribution context
- major invalidation zones
Lower timeframes provide:
- execution timing
- local sweeps
- momentum confirmation
- stop refinement
- entry precision
The system is designed to understand when timeframes agree, conflict, or partially align.
DELTA OS detects and scores market structures such as:
- wedges
- channels
- trendlines
- support/resistance
- compression structures
- expansion structures
- sloping levels
- breakout pressure zones
- liquidity pools
- stop clusters
The system does not expect perfect textbook patterns.
Real market structures are noisy.
So DELTA OS uses fuzzy, probabilistic structural geometry.
Example structural equations:
Resistance:
R(t) = m_R * t + c_R
Support:
S(t) = m_S * t + c_S
Distance from dynamic resistance:
d_R(t) = (R(t) - P(t)) / P(t)
Distance from dynamic support:
d_S(t) = (P(t) - S(t)) / P(t)
3. Liquidity Concepts Engine
DELTA OS converts discretionary trading concepts into measurable features.
It supports concepts such as:
Fair Value Gaps
liquidity sweeps
stop hunts
BOS
CHOCH
order blocks
breaker blocks
mitigation blocks
equal highs/lows
wick rejection
displacement candles
anchored VWAP
session VWAP
volume profile HVN/LVN
previous day/week/month highs and lows
These are converted into:
binary features
continuous scores
probability inputs
risk modifiers
alert triggers
future RL state inputs
Example:
Bullish FVG:
Low[t] > High[t-2]
Example liquidity sweep:
Liquidity Sweep High:
High[t] > previous_high
AND Close[t] < previous_high
4. Probability Engine
The probability engine estimates:
breakout probability
fakeout probability
reversal probability
continuation probability
sweep probability
volatility expansion probability
structural failure probability
Example scoring direction:
Breakout probability increases when:
- touch count >= 4
- volatility compresses
- price closes outside structure
- volume expands
- retest holds
- liquidity sweep occurs before breakout
- higher timeframe confirms
Fakeout risk increases when:
- breakout occurs too early
- volume is weak
- price re-enters the structure
- higher-timeframe resistance is nearby
- spread widens
- execution quality deteriorates
5. Risk Engine
The Risk Engine evaluates:
invalidation levels
ATR stops
structural stops
FVG invalidation
sweep failure exits
volatility risk
liquidity risk
drawdown risk
slippage risk
risk/reward
exposure limits
The Risk Agent can veto a setup.
The Fusion Agent cannot override a Risk Agent veto.
DELTA OS is designed to protect the trader from forcing low-quality trades.
6. Alert Engine
DELTA OS does not produce blind buy/sell calls.
It produces explainable market intelligence alerts.
Example alert types:
compression building
breakout probability rising
fakeout warning
liquidity sweep detected
HTF alignment detected
structure mature after 4th/5th touch
volatility expansion likely
execution quality deteriorating
Example Intelligence Output
RELIANCE — Daily + 4H Compression Alert
Setup:
Falling wedge compression
Scores:
WedgeScore: 0.86
BreakoutProbability: 0.74
FakeoutRisk: 0.31
HTFAlignment: Bullish
ExecutionQuality: Neutral
Reason:
Price has respected upper sloping resistance 5 times.
Volatility is compressing.
Daily structure aligns with weekly support.
Liquidity sweep detected below prior lows.
Action:
Watch for 1H BOS + 15m retest confirmation.
This is the style of intelligence DELTA OS aims to provide.
Not:
“Buy RELIANCE.”
But:
“This structure is maturing, probability is improving, risk is visible, and execution still needs confirmation.”
Multi-Agent Architecture
DELTA OS is designed as a multi-agent market intelligence system.
Each agent has:
clear responsibility
input DTO
output DTO
config section
unit tests
logging
deterministic behavior
replaceable implementation
no UI dependency
no circular dependency
Core Agents
Agent Responsibility
Data Agent Load, validate, clean, and resample market data
Universe Agent Load and filter tradable universes
Timeframe Agents Generate timeframe-specific intelligence
Structural Geometry Agent Detect wedges, channels, trendlines, compression
Liquidity Concepts Agent Detect FVG, sweeps, BOS, CHOCH, order blocks
Market Microstructure Agent Estimate OFI, spread, liquidity, toxicity
Probability Agent Estimate breakout/fakeout/reversal probabilities
Risk Agent Evaluate invalidation, risk/reward, veto conditions
Fusion Agent Combine HTF/LTF intelligence
Ranking Agent Rank opportunities across the market
Alert Agent Generate explainable alerts
Voice Assistant Agent Conversational interface over market intelligence
UI Agent Render dashboard only
Voice Assistant Vision
DELTA OS is designed to support a future voice assistant.
The voice assistant is not a gimmick.
It acts as a conversational interface over the intelligence system.
Example commands:
Delta, show me top compression setups.
Delta, explain RELIANCE on daily and 4H.
Delta, which stocks have high fakeout risk?
Delta, read today’s top alerts.
Delta, is execution valid on 15 minutes?
Delta, what is the invalidation level?
Example response:
DELTA:
RELIANCE has a mature daily compression structure, 4H liquidity sweep confirmation,
and moderate fakeout risk because price is close to higher-timeframe resistance.
Execution is not fully valid yet because 15m retest confirmation is pending.
The voice assistant must remain risk-aware.
It must not bypass the Risk Agent.
It must not place trades without explicit future execution permissions.
Architecture Overview
DELTA OS
├── Data Layer
│ ├── CSV Provider
│ ├── Timeframe Builder
│ ├── Dataset Validator
│ └── Future API Providers
│
├── Intelligence Layer
│ ├── Multi-Timeframe Engines
│ ├── Structural Geometry Engine
│ ├── Liquidity Concepts Engine
│ ├── Market Microstructure Engine
│ ├── Probability Engine
│ ├── Risk Engine
│ └── Fusion Engine
│
├── Agent Layer
│ ├── Data Agent
│ ├── Universe Agent
│ ├── Timeframe Agents
│ ├── Structural Geometry Agent
│ ├── Liquidity Concepts Agent
│ ├── Probability Agent
│ ├── Risk Agent
│ ├── Fusion Agent
│ ├── Ranking Agent
│ ├── Alert Agent
│ ├── Voice Assistant Agent
│ └── UI Agent
│
├── Presentation Layer
│ ├── PySide6 Dashboard
│ ├── Chart Renderer
│ ├── Alert Timeline
│ ├── Multi-Timeframe Table
│ ├── Ranking Dashboard
│ └── Voice Assistant Panel
│
└── Testing Layer
├── Unit Tests
├── Integration Tests
├── Fixtures
└── Deterministic Replay Tests
PySide6 Dashboard Vision
DELTA OS will include a professional desktop dashboard.
The UI should feel like:
a trader cockpit
a market radar
a structural intelligence dashboard
a real-time reasoning engine
Dashboard Areas
Left Sidebar
watchlists
scan profiles
trade profiles
universe selection
filters
Top Status Bar
market status
data provider status
scan latency
alert engine status
risk mode
Center Chart Area
candlestick charts
wedge overlays
channel overlays
trendlines
FVG zones
BOS/CHOCH markers
liquidity sweep markers
HTF zones
invalidation zones
Right Intelligence Panel
DELTA intelligence summary
multi-timeframe table
breakout probability
fakeout risk
risk/reward
execution quality
Bottom Panel
alert timeline
logs
diagnostics
ranking table
scanner activity
Repository Structure
delta_os/
├── pyproject.toml
├── README.md
├── LICENSE
├── .gitignore
│
├── configs/
│ ├── app.yaml
│ ├── scan_profiles.yaml
│ ├── trade_profiles.yaml
│ ├── risk_profiles.yaml
│ └── voice_profiles.yaml
│
├── data/
│ ├── sample/
│ └── fixtures/
│
├── src/
│ └── delta_os/
│ ├── domain/
│ │ ├── entities/
│ │ ├── services/
│ │ └── value_objects/
│ │
│ ├── application/
│ │ ├── dto/
│ │ ├── agents/
│ │ └── use_cases/
│ │
│ ├── infrastructure/
│ │ ├── data/
│ │ ├── voice/
│ │ ├── alerts/
│ │ └── logging/
│ │
│ └── presentation/
│ ├── cli/
│ └── gui/
│
└── tests/
├── unit/
├── integration/
└── fixtures/
Full implementation details are defined in roadmap_docx.docx.
Phase 1 — Offline Structural Intelligence
Repository delivery is now live-market-first and roadmap-aligned.
Deterministic CSV/replay fixtures remain for testing and regression only.
Live data provider support currently includes online polling via
`yahoo_finance`, with broker execution paths safety-gated.
Foundation includes:
live/polling data providers
timeframe builder
swing high/low detector
trendline equation engine
wedge/channel detector
FVG detector
liquidity sweep detector
BOS/CHOCH detector
baseline probability engine
ranking engine
alert model
PySide6 dashboard skeleton
chart overlays
multi-timeframe table
example configs
pytest unit tests
Phase 1 does not include:
broker execution
live order routing
WebSockets
HFT routing
FPGA
live RL execution
low-level C++ execution engine
These are future extension points.
Roadmap
Phase 1 — Offline Structural Intelligence
Build the first working research and dashboard layer using CSV data.
Phase 2 — Advanced Scanner
Add universe scanning, sector filters, richer ranking, and better alert logic.
Phase 3 — Backtesting
Add structural replay, fakeout accuracy testing, breakout follow-through testing, and walk-forward validation.
Phase 4 — Live Data
Add broker/API adapters, WebSocket feeds, and live dashboard updates.
Phase 5 — Voice Assistant
Add speech-to-text, text-to-speech, command routing, transcript panel, and conversational market summaries.
Phase 6 — Microstructure
Add order flow imbalance, spread modeling, slippage estimates, and execution-quality scoring.
Phase 7 — RL Research
Add offline RL environments, PPO/SAC experimentation, risk-aware action spaces, and adaptive execution research.
Phase 8 — Performance Engineering
Add Parquet/Arrow, multiprocessing, async scanning, C++ acceleration points, and low-latency event pipelines.
Development Standards
DELTA OS follows:
Python 3.11+
Clean Architecture
SOLID principles
type hints
DTO-based boundaries
modular agents
pytest
deterministic fixtures
structured logging
config-driven design
no hardcoded symbols
no hardcoded secrets
no UI-domain coupling
Installation
git clone https://github.com/<your-username>/delta_os.git
cd delta_os
python -m venv .venv
Activate environment:
Windows:
.venv\Scripts\activate
Linux/macOS:
source .venv/bin/activate
Install dependencies:
pip install -r requirements.txt
This installs the editable Phase-1 development environment with pytest/dev
tooling and YAML support.
If you want the optional PySide6 dashboard dependencies too:
pip install -e ".[dev,yaml,gui]"
Example CSV Format
timestamp,open,high,low,close,volume,symbol
Example:
timestamp,open,high,low,close,volume,symbol
2024-01-01 09:15:00,100.0,102.0,99.5,101.2,150000,RELIANCE
Usage
Run scanner:
python -m delta_os.presentation.cli.main scan --config configs/app.yaml
Run scanner in operator terminal view:
python -m delta_os.presentation.cli.main scan --config configs/app.yaml --output terminal
Run scanner with online market data (no execution routing):
python -m delta_os.presentation.cli.main scan --config configs/app.yaml --provider yahoo_finance --symbol RELIANCE
Run GUI:
python -m delta_os.presentation.gui.main_window
Run tests:
pytest
Run tests with coverage:
pytest --cov=delta_os
Disclaimer
DELTA OS is a research and decision-support platform.
It does not provide financial advice.
It does not guarantee profits.
It does not replace risk management.
It should not be used for live trading without extensive validation, testing, and compliance review.
Markets involve risk.
Use responsibly.
Final Objective
DELTA OS aims to become:
A professional institutional-style probabilistic market intelligence operating system that scans, reasons, ranks, explains, visualizes, alerts, and evaluates execution quality before a trader acts.
DELTA OS — Intelligence Before Execution