fin-glassbox
Clean Architecture Blueprint
1. System idea
Your framework now has three main intelligence streams:
-
Technical Market Stream Learns temporal market behaviour using an attention-based time-series encoder.
-
Text / Sentiment / News Stream Uses FinBERT for financial text understanding.
-
Fundamental Stream Uses structured company financial data.
These streams feed into:
-
Risk Engine The most important control layer.
-
Fusion Layer Combines all outputs into a final decision.
-
Trade Approval + XAI Produces the final recommendation and explanations.
2. Exact stage-by-stage blueprint
Stage 0 — Input layer
Inputs
- Time-series market data Open, high, low, close, volume, returns, rolling indicators
- Text data News, filings, sentiment-bearing text, macro headlines
- Fundamental data Financial ratios, balance sheet data, earnings metrics
- Cross-asset relation data Correlation graph, sector links, market dependency graph
Stage 1 — Data processing layer
1A. Technical encoder
Model
Shared Temporal Attention Encoder
Purpose
This is your main time-series encoder. It should learn:
- temporal dependencies
- important time windows
- market behaviour patterns
- timing information
Output
A latent market representation used by:
- Technical Analyst
- Volatility Model
- Drawdown Model
- Regime Model
Practical note
Since you want attention but not a massive custom transformer stack, define this block as:
- Temporal Attention Encoder
-
implemented either as:
- lightweight transformer encoder
- attention-augmented sequence model
- fine-tunable pretrained temporal model if available
- otherwise trained on your market history
That keeps your design honest and flexible.
1B. NLP encoder
Model
FinBERT
Purpose
Understands:
- sentiment
- event tone
- company-specific text meaning
- macro news effect
Output
Feeds:
- Sentiment Analyst
- News Analyst
- Regime Model
1C. Fundamental encoder / model
Model options
- MLP
- XGBoost
- LightGBM
- small tabular transformer if you want later
Purpose
Understands:
- company quality
- valuation
- financial strength
- long-term investment potential
Output
Feeds:
- Fundamental Analyst
3. Analyst layer
2A. Technical Analyst
Uses encoded market sequence to output:
- trend score
- momentum score
- timing score
- directional confidence
2B. Sentiment Analyst
Uses FinBERT outputs to determine:
- bullish / bearish sentiment
- sentiment confidence
- text-derived market mood
2C. News Analyst
Uses FinBERT outputs to determine:
- event relevance
- macro/company event impact
- risk relevance from news
2D. Fundamental Analyst
Uses structured company data to determine:
- intrinsic value signals
- quality of business
- undervaluation / overvaluation tendency
4. Risk engine
This is now the strongest part of your architecture.
3A. Volatility Estimation Model
Input
Encoded time-series output
Purpose
Estimate:
- current volatility
- short-term uncertainty
- instability of price movement
Model suggestion
- first version: statistical + model hybrid
- later: larger sequence model if needed
Output
Volatility risk score
3B. Drawdown Risk Model
Input
Encoded time-series output
Purpose
Estimate:
- likely downside from recent peaks
- depth of possible decline
- probability of severe fall
Model suggestion
- LSTM or small sequence model is fine here
Output
Drawdown risk score
3C. Value at Risk (VaR)
Method
Historical VaR
Purpose
Estimate threshold loss under historical distribution.
Output
VaR score / cutoff
3D. Expected Shortfall (CVaR)
Method
Historical CVaR
Purpose
Estimate average tail loss beyond VaR.
Output
Tail-risk severity score
3E. Correlation / Contagion Risk
Model
GNN-based relation model for risk propagation
Purpose
This is where your relational finance modeling lives.
It should capture:
- cross-asset dependency
- sector spillover
- market contagion
- hidden concentration risk
Input
Cross-asset graph built from:
- rolling correlations
- sector / industry links
- maybe co-movement strength
Output
Contagion / dependency risk score
3F. Liquidity Risk Model
Input
Volume, turnover, maybe spread proxies
Purpose
Estimate:
- ease of entering trade
- ease of exiting trade
- slippage risk
- practical tradability
Output
Liquidity score / execution feasibility score
3G. Regime Risk Model
Input
- Temporal attention encoder output
- FinBERT output
Purpose
This model is now a twin bridge between market state and text state.
It should estimate:
- calm / trending / volatile / crisis regime
- whether the current market behaviour state is changing
- how much the environment changes trust in the signal
Output
Market regime classification + regime confidence
3H. Position Sizing Engine
Inputs
All risk submodule outputs:
- volatility
- drawdown
- VaR
- CVaR
- contagion
- liquidity
- regime
Purpose
Convert risk into capital allocation logic.
Output
Recommended:
- no trade
- reduced size
- standard size
- aggressive size only if justified
Model suggestion
Do not start with a big learned model here.
Use:
- rule-based / constrained optimizer first
- later learned sizing if needed
This block must remain interpretable.
5. Analysis split
Qualitative analysis
Comes from:
- Sentiment Analyst
- News Analyst
- Fundamental Analyst
These are your “reasoning-rich” or context-heavy signals.
Quantitative analysis
Comes from:
- Technical Analyst
- All risk engine modules
- Position Sizing Engine
These are your numerical market and risk signals.
6. Fusion layer
You said you will discuss this later with your group, so I’ll keep it architecture-neutral but clean.
Fusion Engine
Role
Combine:
- qualitative analysis
- quantitative analysis
Future implementation options
- learned fusion
- attention-based fusion
- rule-based fusion
- hybrid
Output
- final trade score
- buy / hold / sell
- confidence
- trade size input to final approver
7. Final decision layer
Final Trade Approver
Takes:
- decision
- confidence
- position size recommendation
Returns:
- final action
8. XAI layer
You already decided this correctly.
XAI Layer produces:
- explanation from Technical Analyst
- explanation from FinBERT-based sentiment/news models
- explanation from Fundamental model
- explanation from each risk module
- explanation from fusion layer
- combined final explanation for user
9. Final output
The user sees:
- Buy / Hold / Sell
- Confidence
- Suggested position size
- Risk summary
- Key drivers behind the decision
- Module-wise explanation
- System-level fused explanation
10. Modules
- Shared Temporal Attention Encoder
- FinBERT Financial Text Encoder
- Fundamental Analysis Module
- Technical Analysis Module
- Volatility Risk Module
- Drawdown Risk Module
- Historical VaR Module
- CVaR Tail Risk Module
- GNN Contagion Risk Module
- Liquidity Risk Module
- Regime Detection Module
- Position Sizing Engine
- Qualitative Synthesis Layer
- Quantitative Synthesis Layer
- Fusion Engine
- Final Trade Approver
- XAI Explanation Layer
11. Final compact blueprint
INPUTS
├── Historical Market Data
├── Financial Text Data
├── Fundamental Company Data
└── Cross-Asset Relation Data
ENCODERS
├── Shared Temporal Attention Encoder
├── FinBERT Financial Text Encoder
└── Fundamental Model
ANALYST MODULES
├── Technical Analyst
├── Sentiment Analyst
├── News Analyst
└── Fundamental Analyst
RISK ENGINE
├── Volatility Estimation Model
├── Drawdown Risk Model
├── Historical VaR
├── CVaR
├── GNN Contagion Risk Model
├── Liquidity Risk Model
├── Regime Detection Model
└── Position Sizing Engine
SYNTHESIS
├── Qualitative Analysis
├── Quantitative Analysis
└── Fusion Engine
DECISION
└── Final Trade Approver
EXPLAINABILITY
└── XAI Layer
OUTPUT
├── Buy / Hold / Sell
├── Confidence Score
├── Position Size
├── Risk Summary
└── Final Explanation
Proposed architecture for an explainable distributed financial risk management framework integrating temporal market encoding, financial text understanding, fundamental analysis, multi-component risk estimation, fusion-based decision synthesis, and final explainability output.
