Market-Making System: Comprehensive Analysis & Statement of Work

Date: 2026-01-31
Author: AI Code Review
Status: Phase 1 Ready to Start
Workspace: .builders/0013-market-maker-mvp

Executive Summary

The market-making tool has a solid technical foundation but is approximately 40-50% complete toward stated MVP goals. The core regime detection engine works well, but the Grid Exit Strategy (primary value proposition) is only partially implemented.

CRITICAL ISSUE IDENTIFIED: Hardcoded dummy values in restart gates evaluation make metrics YAMLs untrustworthy. This must be fixed immediately.

Completion Estimate: 180-250 hours total work (9-12 weeks at 20h/week, or 4.5-6 weeks at 40h/week)

Current State Analysis
Critical Gaps Identified
Data Quality Issues (CRITICAL)
Statement of Work
Phase Breakdown
Risk Assessment
Success Criteria

Current State Analysis

What’s Working ✅

1. Regime Detection Engine

Location: repos/market-making/metrics-service/src/regime/

Capabilities:

Hourly OHLCV analysis from KuCoin API
4 regime classifications: RANGE_OK, RANGE_WEAK, TRANSITION, TREND
15+ metrics per analysis:
- Bollinger Band analysis
- Mean reversion strength (OU half-life, Z-scores)
- Volatility metrics (ATR, expansion ratios)
- Trend indicators (swing structure, EMA crossovers)
- Range confidence scores
Git-backed storage in market-maker-data repository

Assessment: ✅ SOLID - Core regime detection logic is well-implemented

2. Infrastructure

Location: repos/market-making/infra/

Components:

Kubernetes CronJob running hourly at :01
ExternalSecrets integration for KuCoin API keys
Docker image build/deploy workflow
ArgoCD deployment patterns
Git-based persistence (no database required)

Assessment: ✅ PRODUCTION-READY - Infrastructure is well-designed

3. Notification System (Partial)

Location: repos/market-making/metrics-service/send_regime_notifications.py

Features:

Pushover integration working
Basic regime alerts functional
Entry evaluator module created
Rate limiting (4h minimum between same-state notifications)

Assessment: ⚠️ PARTIAL - Working but incomplete (see gaps below)

4. Grid Configuration Management

Location: repos/market-making/config/

Features:

YAML-based grid configurations
History tracking via Git
Configuration versioning
Grid state determination from history array

Assessment: ✅ FUNCTIONAL - Config management is solid

Critical Gaps Identified

Gap 1: Grid Exit Strategy - NOT IMPLEMENTED (P0)

Current Status: 30% complete - stub implementation only

Location: repos/market-making/metrics-service/src/exit_strategy/evaluator.py

What Exists:

Basic ExitState enum (NORMAL, WARNING, LATEST_ACCEPTABLE_EXIT, MANDATORY_EXIT)
Simple MANDATORY_EXIT trigger for TREND regime
Basic boundary violation check (≥2 consecutive closes outside range)

What’s Missing:

LATEST_ACCEPTABLE_EXIT Triggers (NOT IMPLEMENTED)

Per spec (.ai/projects/market-making/grid-exit-strategy/spec.md):

❌ TRANSITION persistence tracking (≥2 consecutive 4h bars OR ≥4 consecutive 1h bars)
❌ Mean reversion degradation (OU half-life ≥ 2× baseline)
❌ Volatility expansion ratio > 1.25 threshold
❌ Z-score reversion failure tracking

WARNING Triggers (NOT IMPLEMENTED)

Per spec:

❌ TRANSITION probability ≥ 40% (configurable)
❌ Regime confidence declining over 3 bars
❌ Efficiency Ratio rising above range threshold
❌ Mean reversion speed slowing
❌ Volatility expansion 1.1-1.25× range
❌ Require 2+ conditions to trigger (critical logic)

State Transition Tracking (NOT IMPLEMENTED)

❌ Store previous exit states in Git
❌ Track state durations
❌ Prevent notification spam for same state

Historical Data Loading (NOT IMPLEMENTED)

❌ Load last N metrics files for persistence checks
❌ Cache recent history for performance
❌ Multi-timeframe analysis (1h + 4h bars)

Impact: 🔴 CRITICAL - Cannot trust system to alert when to exit grids, defeating entire purpose

Effort: 50-70 hours

Gap 2: Position Risk Quantification - MISSING (P0)

Current Status: NOT IMPLEMENTED

What’s Missing:

Position Tracking

# DOES NOT EXIST - Need to implement:
class PositionTracker:
    def get_active_positions(self, grid_id: str) -> List[Position]:
        """Fetch actual open orders from KuCoin API"""
        
    def calculate_unrealized_pnl(self, positions: List[Position]) -> float:
        """Current unrealized P&L"""
        
    def get_inventory_imbalance(self, positions: List[Position]) -> float:
        """Fraction of grids stuck on one side"""

Capital Risk Calculator

# DOES NOT EXIST - Need to implement:
class CapitalRiskCalculator:
    def calculate_capital_at_risk(self, inventory, current_price, stop_loss) -> float:
        """Inventory value × (current_price - stop_loss) / current_price"""
        
    def estimate_profit_giveback(self, peak_pnl, current_pnl, delay_hours) -> Tuple[float, float]:
        """Range estimate: [min_giveback, max_giveback]"""
        
    def get_stop_distance_atr(self, current_price, stop_loss, atr) -> float:
        """Distance to stop in ATR units"""

Current Behavior: Risk assessment in history.py only looks at config, not actual positions

Impact: 🔴 CRITICAL - Notifications cannot show:

“Capital at risk: $120.50”
“Expected give-back if delayed 12h: $4-7”
“Stop-loss distance: 0.85 ATR”

Effort: 30-40 hours

Gap 3: Evaluation Cadence - WRONG (P1)

Current: Hourly (at :01 past the hour)
Required: Every 15 minutes

Impact: ⚠️ MEDIUM

45-minute blind spot between evaluations
Could miss rapid regime transitions
Doesn’t meet “MANDATORY_EXIT → Immediately” requirement

Fix: Change CronJob from 1 * * * * to 1,16,31,46 * * * *

Effort: 0.5 hours

Gap 4: Audit Logging - MISSING (P2)

Current Status: NOT IMPLEMENTED

Required (per requirements):

Log all exit signals
Log operator responses (or lack thereof)
Track response time vs recommended window
Enable retrospective analysis of signal quality

Impact: 🟡 LOW - Cannot measure system effectiveness over time

Effort: 3-4 hours

Gap 5: Testing - NEARLY ZERO (P0)

Current Status: Minimal test coverage

Location: repos/market-making/metrics-service/tests/

What Exists:

Some integration test stubs
No unit tests for exit strategy
No backtesting capability

Required:

Unit tests for all metric calculations
Unit tests for exit trigger logic
Integration tests (regime → exit → notification flow)
Backtesting framework to validate signal quality

Impact: 🔴 CRITICAL - Cannot refactor or trust changes

Effort: 40-50 hours

Data Quality Issues (CRITICAL)

The Smoking Gun: Hardcoded Dummy Values

Location: repos/market-making/metrics-service/src/regime/engine.py

Lines 268-280 (and duplicated at 349-359):

# Mock values for now - these should come from actual analysis
# TODO: Extract these from the detailed_analysis once refined classification is implemented
trend_score = regime_state.trend_score or 50.0
mean_rev_score = regime_state.mean_rev_score or 50.0
adx = 25.0  # TODO: Extract from analysis
adx_history = [25.0] * 10  # TODO: Extract from analysis
normalized_slope = 0.1  # TODO: Extract from analysis
efficiency_ratio = 0.4  # TODO: Extract from analysis
lag1_autocorr = -0.1  # TODO: Extract from analysis
ou_half_life = 24.0  # TODO: Extract from analysis
atr = 1500.0  # TODO: Extract from analysis
atr_history = [1500.0] * 100  # TODO: Extract from analysis
bb_bandwidth = 0.02  # TODO: Extract from analysis
bb_bandwidth_history = [0.02] * 10  # TODO: Extract from analysis

Impact Analysis

Why This is Critical:

Restart gates evaluation depends on these metrics
Grid creation recommendations use these values
Exit strategy (when implemented) would use these values
Metrics YAMLs contain fake data - cannot trust historical analysis
No way to validate regime classifications with dummy data

Affected Components:

Restart gates (Gate 1: Directional Energy Decay, Gate 2: Mean Reversion Return, Gate 3: Tradable Volatility)
Risk assessment in notifications
Historical backtesting (impossible with fake data)

User Impact:

“I often don’t trust the generated metrics yamls” - This is why!

Missing Calculations

Need to implement:

ADX (Average Directional Index)
- Measures trend strength
- Range: 0-100 (>25 = trending, <20 = ranging)
- Formula: Smoothed average of Directional Movement
Normalized Slope
- Price slope normalized by ATR
- Measures trend direction relative to volatility
- Formula: (current_price - price_N_bars_ago) / (ATR * N)
Efficiency Ratio (Perry Kaufman)
- Measures trend efficiency
- Range: 0-1 (higher = more trending)
- Formula: |net_change| / sum(abs(bar_changes))
Lag-1 Autocorrelation
- Measures mean reversion
- Range: -1 to 1 (negative = mean reverting)
- Formula: Pearson correlation of price[t] vs price[t-1]
Ornstein-Uhlenbeck Half-Life
- Time for price to revert halfway to mean
- Critical for grid viability
- Formula: -log(2) / log(ar_coefficient)
Bollinger Band Bandwidth
- Normalized volatility measure
- Formula: (upper_band - lower_band) / middle_band

Statement of Work

Phase 1: Data Trust & Quality (CRITICAL - P0)

Objective: Remove all hardcoded dummy values and implement real metric calculations

Duration: 2-3 weeks (40-60 hours)

Priority: 🔴 P0 - BLOCKER - Must complete before any other work

Tasks

1.1 Implement Missing Metric Calculations (20-30h)

Deliverables:

src/regime/metrics/adx.py - ADX calculation
src/regime/metrics/efficiency_ratio.py - Efficiency Ratio
src/regime/metrics/autocorrelation.py - Lag-1 autocorrelation
src/regime/metrics/ou_process.py - OU half-life estimation
src/regime/metrics/slope.py - Normalized slope
src/regime/metrics/bollinger.py - BB bandwidth calculation

Acceptance Criteria:

Each metric has dedicated module with docstrings
Input validation (e.g., ATR can’t be negative)
Output validation (e.g., correlation ∈ [-1, 1])
Type hints throughout

1.2 Extract Metrics from Regime Analysis (8-12h)

Current Flow:

regime_state = classify_regime(price_data)
# ❌ Use hardcoded values
adx = 25.0

Target Flow:

regime_state = classify_regime(price_data)
detailed_analysis = regime_state.detailed_analysis
# ✅ Extract real values
adx = detailed_analysis.get('adx', {}).get('current', None)
if adx is None:
    raise ValueError("ADX not calculated in regime analysis")

Files to Modify:

src/regime/engine.py (lines 268-280, 349-359)
src/regime/classifier.py (enhance to calculate all metrics)

Acceptance Criteria:

All 10 hardcoded values replaced with real calculations
No fallback to dummy values (fail fast if metrics missing)
Logging shows which metrics were calculated

1.3 Add Data Validation (8-12h)

Create: src/regime/validation/schema_validator.py

Validation Rules:

metrics_schema:
  adx:
    type: float
    range: [0, 100]
    required: true
  efficiency_ratio:
    type: float
    range: [0, 1]
    required: true
  lag1_autocorr:
    type: float
    range: [-1, 1]
    required: true
  ou_half_life:
    type: float
    range: [0.1, 1000]  # hours
    required: true
  atr:
    type: float
    min: 0
    required: true

Features:

Schema validation for all metrics YAMLs
Sanity checks per metric type
Automated validation on every metrics write

Acceptance Criteria:

All metrics YAMLs validated before Git commit
Validation errors logged with details
CI/CD fails on invalid metrics

1.4 Create Data Quality Dashboard (4-6h)

Create: src/regime/quality/dashboard.py

Features:

Visual indicators: ✅ Real data vs ⚠️ Dummy data
Historical trend validation (detect sudden jumps)
Anomaly detection (e.g., ADX stuck at 25.0)
Comparison: before/after Phase 1

Output: HTML report showing data quality metrics

Acceptance Criteria:

Dashboard shows 100% real data after Phase 1
Can identify dummy data in historical files
Anomaly detection catches obvious errors

1.5 Unit Tests (8-12h)

Test Coverage Required:

ADX calculation: 5+ test cases (trending, ranging, edge cases)
Efficiency Ratio: 4+ test cases
Autocorrelation: 3+ test cases
OU half-life: 4+ test cases (including non-stationary rejection)
Normalized slope: 3+ test cases
BB bandwidth: 3+ test cases

Test Data:

Known input/output pairs (validated manually)
Edge cases (empty data, single point, NaN handling)
Real market data samples

Acceptance Criteria:

90%+ code coverage for new metric modules
All tests passing
Tests run in CI/CD

Total Phase 1 Effort: 40-60 hours

Phase 2: Complete Grid Exit Strategy (HIGH PRIORITY - P0)

Objective: Implement all missing exit triggers and state tracking

Duration: 2-3 weeks (50-70 hours)

Priority: 🔴 P0 - BLOCKER - Core value proposition

Tasks

2.1 LATEST_ACCEPTABLE_EXIT Triggers (8-12h)

Implement: src/exit_strategy/triggers/latest_acceptable.py

Requirements:

TRANSITION Persistence Tracking

def check_transition_persistence(history: List[Dict]) -> Tuple[bool, str]:
    """
    Trigger if:
    - ≥2 consecutive 4h bars with TRANSITION verdict, OR
    - ≥4 consecutive 1h bars with TRANSITION verdict
    
    Args:
        history: List of recent metrics (last 12 hours)
    
    Returns:
        (triggered: bool, reason: str)
    """

Mean Reversion Degradation

def check_mean_reversion_degradation(
    current_half_life: float,
    baseline_half_life: float,
    threshold_multiplier: float = 2.0
) -> Tuple[bool, str]:
    """
    Trigger if OU half-life ≥ 2× baseline
    
    Baseline = 7-day rolling average during RANGE_OK
    """

Volatility Expansion

def check_volatility_expansion(
    current_atr: float,
    baseline_atr: float,
    threshold: float = 1.25
) -> Tuple[bool, str]:
    """
    Trigger if volatility expansion ratio > 1.25
    """

Z-Score Reversion Failure

def check_zscore_reversion_failure(
    price_history: List[float],
    lookback_bars: int = 6
) -> Tuple[bool, str]:
    """
    Trigger if Z-score excursions fail to revert within expected bars
    """

Acceptance Criteria:

All 4 trigger functions implemented
Each trigger independently testable
Configurable thresholds via YAML
Unit tests for each trigger

2.2 WARNING Triggers (4-6h)

Implement: src/exit_strategy/triggers/warning.py

Requirements:

Require 2+ conditions to trigger WARNING:

TRANSITION probability ≥ 40% (configurable)
Regime confidence declining over 3 bars
Efficiency Ratio rising above 0.6 (configurable)
Mean reversion speed slowing
Volatility expansion 1.1-1.25×

Logic:

def evaluate_warning_conditions(regime_history: List[Dict], config: Dict) -> ExitEvaluation:
    """
    Evaluate all warning conditions.
    
    Returns WARNING if 2+ conditions met, else NORMAL.
    """
    conditions_met = []
    
    # Check each condition...
    if transition_probability >= config['warning_transition_threshold']:
        conditions_met.append("TRANSITION probability rising")
    
    # ... check others ...
    
    if len(conditions_met) >= 2:
        return ExitState.WARNING, conditions_met
    else:
        return ExitState.NORMAL, ["Single warning condition - not actionable"]

Acceptance Criteria:

2+ conditions required to trigger
All 5 condition checks implemented
Configurable thresholds
Unit tests covering edge cases (1 condition, 2 conditions, all conditions)

2.3 State Transition Tracking (4-6h)

Implement: src/exit_strategy/state_tracker.py

Features:

State History in Git

market-maker-data/
  exit_states/
    ETH-USDT/
      2026-01-31.json  # Daily state log

Track Transitions

{
  "transitions": [
    {
      "timestamp": "2026-01-31T14:23:00Z",
      "from_state": "NORMAL",
      "to_state": "WARNING",
      "reasons": ["TRANSITION probability rising", "Confidence declining"],
      "regime_verdict": "RANGE_WEAK",
      "confidence": 0.48
    }
  ]
}

Prevent Notification Spam
- Max 1 WARNING per 4h for same grid
- Max 1 LATEST_ACCEPTABLE_EXIT per 2h
- Max 1 MANDATORY_EXIT per 1h
- Track last notification timestamp

Acceptance Criteria:

State transitions logged to Git
Rate limiting prevents spam
Can query: “When did we last alert for this grid?”
Unit tests for rate limiting logic

2.4 Historical Data Loading (4-6h)

Implement: src/exit_strategy/history_loader.py

Features:

Load Last N Metrics Files

def load_recent_metrics(
    symbol: str,
    hours: int = 12,
    data_repo: Path
) -> List[Dict]:
    """
    Load last N hours of metrics for persistence checks
    
    Returns sorted list (oldest first)
    """

Multi-Timeframe Analysis

def get_4h_bars(metrics_history: List[Dict]) -> List[Dict]:
    """Extract 4h bar data for structural confirmation"""
    
def get_1h_bars(metrics_history: List[Dict]) -> List[Dict]:
    """Extract 1h bar data for rapid detection"""

Caching for Performance
- Cache last 24h of metrics in memory
- Invalidate cache on new metrics arrival
- Reduce Git reads

Acceptance Criteria:

Can load last 12-24 hours of metrics
Multi-timeframe extraction works
Caching reduces duplicate reads
Unit tests with mock file system

2.5 Integration & Testing (8-12h)

Tasks:

Wire up all triggers in evaluator
- Update ExitStateEvaluator.evaluate() to use new triggers
- Ensure correct priority: MANDATORY → LATEST_ACCEPTABLE → WARNING → NORMAL
Integration tests
- Test full flow: metrics → history load → trigger eval → state classification
- Test state transitions: NORMAL → WARNING → LATEST_ACCEPTABLE → MANDATORY
- Test notification prevention (rate limiting)
Real data validation
- Run against last 7 days of actual metrics
- Verify exit states make sense
- Check for false positives/negatives

Acceptance Criteria:

All triggers integrated
Integration tests passing
Manual validation against real data shows reasonable behavior

2.6 Configuration & Documentation (4-6h)

Create: config/exit_strategy_config.yaml

exit_rules:
  latest_acceptable_exit:
    transition_persistence_4h_bars: 2
    transition_persistence_1h_bars: 4
    mean_reversion_halflife_multiplier: 2.0
    volatility_expansion_threshold: 1.25
    zscore_reversion_failure_bars: 6
    
  warning:
    transition_probability_threshold: 0.40
    regime_confidence_decline_bars: 3
    efficiency_ratio_threshold: 0.6
    volatility_expansion_min: 1.10
    volatility_expansion_max: 1.25
    
  mandatory_exit:
    consecutive_closes_outside_range: 2
    directional_swing_bars: 6
    stop_loss_buffer_atr: 0.1
    
notifications:
  rate_limits:
    warning_min_hours: 4
    latest_acceptable_min_hours: 2
    mandatory_min_hours: 1

Documentation:

Trigger logic explained
Configuration guide
Tuning recommendations

Acceptance Criteria:

All thresholds configurable
Configuration validated on load
Documentation complete

Total Phase 2 Effort: 50-70 hours

Phase 3: Position Risk Quantification (MEDIUM PRIORITY - P1)

Objective: Add real position tracking and capital risk calculations

Duration: 1-2 weeks (30-40 hours)

Priority: 🟡 P1 - Enhances notifications but not blocking

Tasks

3.1 KuCoin Position Tracker (8-12h)

Implement: src/position/tracker.py

Features:

Fetch Active Positions

class PositionTracker:
    def __init__(self, kucoin_client: KuCoinExchange):
        self.client = kucoin_client
    
    def get_active_grid_orders(self, grid_id: str) -> List[GridOrder]:
        """
        Fetch all open orders for a grid from KuCoin API
        
        Returns:
            List of GridOrder objects with price, size, side, etc.
        """

Calculate Unrealized PnL

def calculate_unrealized_pnl(
    self,
    orders: List[GridOrder],
    current_price: float
) -> float:
    """
    Calculate unrealized P&L based on current positions
    
    Formula: Sum of (current_price - entry_price) * size for all positions
    """

Inventory Imbalance

def get_inventory_imbalance(self, orders: List[GridOrder]) -> float:
    """
    Calculate fraction of grids stuck on one side
    
    Returns: 
        Value in [-1, 1] where:
        -1 = all short positions (bought too much)
        +1 = all long positions (sold too much)
        0 = balanced
    """

Acceptance Criteria:

Successfully fetches positions from KuCoin API
Calculates accurate PnL (validate against KuCoin UI)
Handles edge cases (no positions, API errors)
Unit tests with mocked KuCoin responses

3.2 Capital Risk Calculator (6-8h)

Implement: src/position/risk_calculator.py

Features:

Capital at Risk

def calculate_capital_at_risk(
    inventory: List[GridOrder],
    current_price: float,
    stop_loss: float
) -> float:
    """
    Calculate capital at risk if stop-loss hit
    
    Formula:
        For each position:
            risk = position_value × abs((current_price - stop_loss) / current_price)
        
        Total risk = sum(all positions)
    
    Returns:
        Dollar amount at risk
    """

Profit Give-Back Estimation

def estimate_profit_giveback(
    peak_pnl: float,
    current_pnl: float,
    delay_hours: int,
    volatility_atr: float
) -> Tuple[float, float]:
    """
    Estimate profit give-back if exit delayed
    
    Returns:
        (min_giveback, max_giveback) range in dollars
        
    Assumptions:
        - Price continues trending at 0.5-1.0 × ATR per hour
        - Min scenario: slow drift (0.5 ATR/h)
        - Max scenario: acceleration (1.0 ATR/h)
    """

Stop-Loss Distance in ATR

def get_stop_distance_atr(
    current_price: float,
    stop_loss: float,
    atr: float
) -> float:
    """
    Calculate distance to stop-loss in ATR units
    
    Returns:
        Number of ATR units to stop (e.g., 0.85 means 0.85 ATR away)
    """

Acceptance Criteria:

All 3 risk calculations implemented
Formulas validated against manual calculations
Edge case handling (no inventory, zero ATR, etc.)
Unit tests with known scenarios

3.3 Enhance Notifications (4-6h)

Modify: send_regime_notifications.py

Add to notifications:

WARNING Alert

⚠️ ETH/USDT Grid WARNING

Regime: TRANSITION probability rising (45%)
Issues: Mean reversion slowing, volatility expanding (1.15x)

Position:
• Unrealized profit: $12.34
• Capital at risk: $45.67

Action: Review grid within 24h

LATEST_ACCEPTABLE_EXIT Alert

⏳ ETH/USDT - LATEST ACCEPTABLE EXIT

Grid assumptions failing:
• TRANSITION persists 4 bars (4 hours)
• Mean reversion half-life 2.3x baseline

Position:
• Unrealized profit: $12.34
• Est. give-back if delayed 12h: $4.00-$7.00 (30-50% of profit)
• Stop-loss distance: 0.85 ATR
• Capital at risk: $120.50

Action: STOP GRID within 4-12 hours to preserve 75-90% of profit

MANDATORY_EXIT Alert

🛑 ETH/USDT - MANDATORY EXIT

TREND DETECTED - STOP GRID IMMEDIATELY

Trigger: 2 consecutive closes outside range bounds

Position:
• Capital at risk: $120.50
• Stop-loss distance: 0.6 ATR (CRITICAL)
• Unrealized profit: $12.34 (will become loss if trend continues)

ACTION REQUIRED: Stop grid NOW to protect capital

Acceptance Criteria:

All notification templates updated
Risk metrics included in every alert
Formatting clear and actionable
Integration tests validate notification content

3.4 Error Handling & Graceful Degradation (4-6h)

Requirements:

KuCoin API Failures
- Retry logic with exponential backoff
- Fallback to last known position if API down
- Log errors but don’t stop exit evaluation

Missing Position Data

If can’t fetch positions, show in notification:

Position: Unable to fetch from KuCoin API
(Exit evaluation based on regime only)

Circuit Breaker
- After 3 consecutive API failures, skip position tracking
- Resume after 15 minutes

Acceptance Criteria:

Graceful degradation on API failures
Exit evaluation continues even without position data
Clear indication in notifications when data missing
Unit tests for error scenarios

3.5 Testing & Validation (8-12h)

Test Coverage:

Unit Tests
- Position fetching with mocked KuCoin API
- Risk calculations with known inputs
- Notification formatting
- Error handling scenarios
Integration Tests
- Full flow: fetch positions → calculate risk → generate notification
- Test with real KuCoin testnet (if available)
- Validate against actual grid positions
Manual Validation
- Compare PnL calculations with KuCoin UI
- Validate capital-at-risk with manual math
- Review notification content with real data

Acceptance Criteria:

80%+ code coverage for new modules
Integration tests passing
Manual validation confirms accuracy

Total Phase 3 Effort: 30-40 hours

Phase 4: Testing & Validation (CRITICAL - P0)

Objective: Comprehensive test coverage and backtesting validation

Duration: 1 week (40-50 hours)

Priority: 🔴 P0 - Cannot deploy without tests

Tasks

4.1 Unit Tests for Metric Calculations (8-10h)

Coverage Required:

ADX Calculation (5 test cases)
- Trending market (ADX > 40)
- Ranging market (ADX < 20)
- Edge case: insufficient data
- Edge case: flat price (zero movement)
- Validate against known indicators (TradingView, TA-Lib)
Efficiency Ratio (4 test cases)
- Strong trend (ER > 0.8)
- Weak trend (ER < 0.3)
- Edge case: zero net change
- Validate formula implementation
Autocorrelation (3 test cases)
- Mean reverting series (negative correlation)
- Trending series (positive correlation)
- Random walk (near zero)
OU Half-Life (4 test cases)
- Fast mean reversion (half-life < 10h)
- Slow mean reversion (half-life > 50h)
- Non-stationary rejection (no half-life)
- Edge case: perfect mean reversion
Normalized Slope (3 test cases)
- Uptrend
- Downtrend
- Sideways
BB Bandwidth (3 test cases)
- High volatility (wide bands)
- Low volatility (narrow bands)
- Edge case: zero volatility

Test Data Sources:

Manually calculated examples
Known market scenarios (2020 crash, 2021 bull run)
Synthetic data with known properties

Acceptance Criteria:

All 22 test cases implemented
Tests pass consistently
Coverage ≥ 90% for metric modules

4.2 Unit Tests for Exit Triggers (10-12h)

Coverage Required:

MANDATORY_EXIT Triggers (8 test cases)
- TREND regime detected
- 2 consecutive closes outside range
- Directional structure (HH/HL pattern)
- Stop-loss breached
- Edge: 1 close outside (should NOT trigger)
- Edge: TRANSITION but no closes outside
- Edge: Boundary violation but reverses
- Multiple triggers active simultaneously
LATEST_ACCEPTABLE_EXIT Triggers (10 test cases)
- TRANSITION persists 2× 4h bars
- TRANSITION persists 4× 1h bars
- OU half-life ≥ 2× baseline
- Volatility expansion > 1.25
- Z-score fails to revert
- Edge: TRANSITION for 1 bar only (should NOT trigger)
- Edge: Half-life exactly 2× baseline
- Edge: Volatility 1.24× (just below threshold)
- Multiple triggers active
- Historical data insufficient
WARNING Triggers (8 test cases)
- 2 conditions met (minimum)
- 3 conditions met
- All 5 conditions met
- Only 1 condition (should NOT trigger)
- TRANSITION probability exactly 40%
- Confidence declining over 3 bars
- ER rising above threshold
- Volatility expanding in warning range

Acceptance Criteria:

All 26 test cases implemented
Tests cover edge cases (boundary conditions)
Coverage ≥ 90% for trigger modules

4.3 Integration Tests (10-12h)

Test Scenarios:

End-to-End Flow (5 scenarios)

Scenario 1: NORMAL → WARNING → LATEST_ACCEPTABLE_EXIT → MANDATORY_EXIT
Scenario 2: NORMAL → WARNING → back to NORMAL (false alarm)
Scenario 3: NORMAL → MANDATORY_EXIT (rapid trend)
Scenario 4: LATEST_ACCEPTABLE_EXIT → operator exits → reset
Scenario 5: Notification rate limiting prevents spam

Multi-Timeframe Analysis
- 1h bars detect rapid transitions
- 4h bars provide structural confirmation
- Conflicting signals resolved correctly
Git Integration
- Metrics stored correctly
- State transitions logged
- History loading works across date boundaries
Notification Delivery
- Pushover receives correct messages
- Rate limiting works
- Error handling (API down)

Test Setup:

Mock market-maker-data Git repo
Fake Pushover API endpoint
Synthetic metrics spanning multiple days

Acceptance Criteria:

All 5 end-to-end scenarios pass
Integration tests run in CI/CD
Tests use isolated environment (no prod data)

4.4 Backtesting Framework (12-16h)

Objective: Validate exit strategy would have preserved profits historically

Implement: backtest/regime_exit_backtest.py

Features:

Replay Historical Metrics

class RegimeExitBacktest:
    def __init__(self, metrics_path: Path, start_date: str, end_date: str):
        """Load historical metrics for backtest period"""
    
    def run(self) -> BacktestResults:
        """
        Simulate exit strategy on historical data
        
        For each hour:
            1. Load metrics
            2. Evaluate exit state
            3. Record would-be action
            4. Calculate profit preservation
        
        Returns:
            Summary of exit quality, profit preservation, signal accuracy
        """

Exit Quality Metrics

@dataclass
class BacktestResults:
    total_exit_signals: int
    mandatory_exits: int
    latest_acceptable_exits: int
    warnings: int
    
    # Profit preservation
    avg_profit_retention_ratio: float  # Target: ≥ 0.75
    peak_profit_captured_pct: float
    
    # Timeliness
    avg_exit_lead_time_hours: float  # Before trend confirmed
    exits_before_stop_loss_pct: float  # Target: ≥ 95%
    
    # Accuracy
    true_transition_detection_rate: float  # Target: ≥ 70%
    false_exit_rate: float  # Target: ≤ 30%

Scenario Analysis
- 2024 ETH Range: Sep-Nov (should show NORMAL mostly)
- 2024 ETH Breakout: Dec (should trigger exits)
- 2025 Volatility Spike: Jan (should warn/exit)

Validation Criteria:

Did exit signals fire before major trends?
Would we have preserved ≥75% of peak profit?
False positive rate acceptable (≤30%)?

Acceptance Criteria:

Backtesting framework working
Run on 3+ historical scenarios
Results show system would have worked
Report generated with charts

4.5 CI/CD Integration (4-6h)

Setup:

GitHub Actions Workflow

# .github/workflows/test-metrics-service.yml
name: Metrics Service Tests
 
on: [push, pull_request]
 
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.12'
      - name: Install dependencies
        run: |
          cd repos/market-making/metrics-service
          pip install -r requirements.txt
          pip install pytest pytest-cov
      - name: Run tests
        run: |
          cd repos/market-making/metrics-service
          pytest --cov=src --cov-report=xml --cov-report=html
      - name: Upload coverage
        uses: codecov/codecov-action@v2

Pre-commit Hooks
- Run unit tests before commit
- Lint with black/flake8
- Type check with mypy
Quality Gates
- Minimum 80% code coverage
- All tests must pass
- No lint errors

Acceptance Criteria:

CI/CD pipeline running
Tests execute on every commit
Coverage reports generated
Quality gates enforced

Total Phase 4 Effort: 40-50 hours

Phase 5: Operational Improvements (MEDIUM - P2)

Objective: Production readiness and observability

Duration: 1 week (20-30 hours)

Priority: 🟡 P2 - Nice to have but not blocking

Tasks

5.1 15-Minute Evaluation Cadence (0.5h)

Current: 1 * * * * (hourly at :01)
Target: 1,16,31,46 * * * * (every 15 minutes)

Files to Modify:

repos/market-making/infra/metrics-service/cronjob.yaml

# Before
schedule: "1 * * * *"
 
# After
schedule: "1,16,31,46 * * * *"

Alternative: Create separate CronJob for exit evaluation

Metrics collection: Hourly (heavy API usage)
Exit evaluation: Every 15 minutes (reads from Git)

Acceptance Criteria:

CronJob runs every 15 minutes
Logs show 4× evaluations per hour
No API rate limiting issues

5.2 Audit Logging (3-4h)

Implement: src/exit_strategy/audit_logger.py

Log Format:

# market-maker-data/exit_events/YYYY/MM/DD/HH-MM-SYMBOL.yaml
event_id: evt_2026-01-31T14:23:00_eth
grid_id: eth-v3
symbol: ETH/USDT
timestamp: "2026-01-31T14:23:00Z"
 
exit_state:
  current: LATEST_ACCEPTABLE_EXIT
  previous: WARNING
  transition_time: "2026-01-31T14:23:00Z"
 
triggers:
  - type: TRANSITION_PERSISTENCE
    description: "TRANSITION verdict for 4 consecutive 1h bars"
    bars_affected: [2026-01-31T11:00, 2026-01-31T12:00, ...]
  - type: MEAN_REVERSION_DEGRADATION
    description: "OU half-life 2.3x baseline"
    current_halflife_minutes: 1840
    baseline_halflife_minutes: 800
 
position_risk:
  capital_at_risk: 120.50
  unrealized_pnl: 12.34
  stop_loss_distance_atr: 0.85
 
notifications_sent:
  - channel: push
    status: delivered
    sent_at: "2026-01-31T14:23:15Z"
    delivery_confirmed_at: "2026-01-31T14:23:16Z"
 
operator_action:
  action_taken: null  # To be updated manually
  action_time: null
  reaction_time_seconds: null
  notes: null

Features:

Log every exit state transition
Log notification attempts (success/failure)
Placeholder for operator action (manual update)
Git-backed storage

Acceptance Criteria:

All transitions logged
Logs readable and parseable
Can query: “Show all MANDATORY_EXIT events last 30 days”
Unit tests for logger

5.3 KPI Tracking (4-6h)

Implement: src/exit_strategy/kpis.py

KPIs to Track (per .ai/projects/market-making/new-instructions.md):

Exit Within Acceptable Window (EAW%)
- Formula: ExitsBeforeMandatory / TotalExitEvents
- Target: ≥ 90%
Profit Retention Ratio (PRR)
- Formula: RealizedProfitAtExit / MaxUnrealizedProfitBeforeExit
- Target: ≥ 0.75
Stop-Loss Avoidance Rate (SLAR)
- Formula: ExitsBeforeStop / TotalGridsStopped
- Target: ≥ 95%
True Transition Detection Rate (TTDR)
- Formula: TransitionExitsWithFollowThrough / TotalTransitionExits
- Target: ≥ 70%
Mandatory Exit Compliance (MEC%)
- Formula: CompliedMandatoryExits / MandatoryExitSignals
- Target: 100%

Monthly Report:

def generate_monthly_kpi_report(year: int, month: int) -> KPIReport:
    """
    Aggregate all exit events for the month and calculate KPIs
    
    Returns:
        KPIReport with metrics, charts, recommendations
    """

Acceptance Criteria:

All 5 KPIs calculable from audit logs
Monthly report generation works
Can track KPI trends over time
Unit tests for KPI calculations

5.4 Documentation (4-6h)

Create:

Operational Runbook (docs/ops/runbook.md)
- How to deploy
- How to monitor
- How to troubleshoot
- Emergency procedures
Troubleshooting Guide (docs/ops/troubleshooting.md)
- Common errors and solutions
- API failures
- Notification not received
- Exit signals not firing
Metrics Interpretation Guide (docs/metrics_guide.md)
- What each metric means
- How to interpret exit states
- When to override system recommendations
- Tuning thresholds
Configuration Reference (docs/configuration.md)
- All config parameters explained
- Recommended values
- How to tune for different markets

Acceptance Criteria:

All 4 documents complete
Reviewed by operator (you)
Examples included
Links to relevant code

5.5 Monitoring & Alerting (4-6h)

Setup:

Prometheus Metrics
- Exit state distribution (gauge)
- Notification success rate (counter)
- API call latency (histogram)
- Error rate (counter)
Grafana Dashboard
- Exit state timeline
- Notification delivery status
- System health (API errors, latency)
- KPI trends
Alerts
- No metrics collected in 2 hours → Alert
- Notification delivery failure → Alert
- API error rate > 10% → Alert

Acceptance Criteria:

Prometheus metrics exposed
Grafana dashboard working
Alerts configured and tested
Can diagnose issues from dashboard

5.6 Performance Optimization (4-6h)

Optimizations:

Caching
- Cache last 24h of metrics in memory
- Reduce Git reads by 90%
Async Processing
- Fetch position data async
- Send notifications async
- Don’t block exit evaluation
Database for State (optional)
- Consider SQLite for state tracking
- Faster than Git for queries
- Git remains source of truth

Benchmarks:

Exit evaluation: < 30 seconds per grid
Notification delivery: < 60 seconds
Git commit/push: < 10 seconds

Acceptance Criteria:

Performance benchmarks met
No degradation with multiple grids
Load testing passed (5 concurrent grids)

Total Phase 5 Effort: 20-30 hours

Risk Assessment

Technical Risks

Risk	Impact	Likelihood	Mitigation
Metric calculations incorrect	High	Medium	• Extensive unit tests • Validation against known indicators • Manual verification with TradingView
False MANDATORY_EXIT signals	High	Medium	• Require multiple confirming indicators • Tune thresholds via backtesting • Track False Exit Rate KPI
Missed regime transitions	High	Low	• 15-min cadence • Multi-timeframe confirmation • Conservative thresholds
KuCoin API rate limiting	Medium	Low	• Cache position data • Implement backoff strategy • Monitor API usage
Git push failures	Medium	Low	• Retry logic with exponential backoff • Local backup before push • Alert on persistent failures

Operational Risks

Risk	Impact	Likelihood	Mitigation
Operator misses notification	High	Medium	• Multi-channel delivery (Pushover + Email) • Escalating urgency for MANDATORY_EXIT • Track Mandatory Exit Compliance KPI
Notification fatigue	Medium	High	• Smart rate limiting by exit state • Clear urgency indicators • Only actionable alerts
Grid stopped unnecessarily	Medium	Medium	• Backtesting validates signal quality • Track False Exit Rate • Tunable thresholds
Phase 1 reveals more data issues	Medium	Medium	• Allocate buffer time (20% contingency) • Iterative approach • Daily code review

Schedule Risks

Risk	Impact	Likelihood	Mitigation
Phase 1 takes longer than estimated	High	Medium	• Priority-based execution • Can proceed with partial completion • Daily progress tracking
Testing reveals major bugs	Medium	Low	• Test early and often • Integration tests during development • Code review before merge
Scope creep	Medium	Medium	• Strict adherence to SOW • Document future enhancements separately • Phase gates

Success Criteria

Phase 1 (Data Quality) - Complete When:

✅ All hardcoded values replaced

Zero TODOs in regime/engine.py lines 268-359
All metrics calculated from real data
No fallback to dummy values

✅ All metric calculations implemented

ADX, Efficiency Ratio, Autocorrelation, OU half-life, Normalized slope, BB bandwidth
Unit tests passing (90%+ coverage)
Validated against known indicators

✅ Data validation in place

Schema validator working
All metrics YAMLs validated before commit
Quality dashboard shows 100% real data

✅ Can trust metrics YAMLs

Manual inspection of recent files shows real data
Anomaly detection catches issues
User (Craig) confirms trust restored

Phase 2 (Exit Strategy) - Complete When:

✅ All exit triggers implemented

MANDATORY_EXIT: 4 trigger types working
LATEST_ACCEPTABLE_EXIT: 4 trigger types working
WARNING: 5 condition checks, require 2+ to trigger
Unit tests covering all edge cases

✅ State tracking working

Transitions logged to Git
Rate limiting prevents spam
Can query historical state

✅ Historical data loading

Load last 12-24 hours of metrics
Multi-timeframe analysis (1h + 4h)
Caching reduces duplicate reads

✅ Integration validated

End-to-end tests passing
Manual testing with real data looks good
Configuration complete and documented

Phase 3 (Position Risk) - Complete When:

✅ Position tracking working

Successfully fetches from KuCoin API
PnL matches KuCoin UI
Graceful error handling

✅ Risk calculations accurate

Capital-at-risk calculation validated
Profit give-back estimates reasonable
Stop-loss distance correct

✅ Notifications enhanced

All templates include risk metrics
Clear and actionable
Tested with real positions

Phase 4 (Testing) - Complete When:

✅ Comprehensive test coverage

Unit tests: 80%+ coverage
Integration tests: all scenarios passing
Backtesting: validates system would work

✅ Backtesting shows success

Profit retention ratio ≥ 0.75 on historical data
Stop-loss avoidance ≥ 95%
False exit rate ≤ 30%

✅ CI/CD pipeline working

Tests run on every commit
Quality gates enforced
Coverage reports generated

Phase 5 (Operational) - Complete When:

✅ 15-min cadence running

CronJob executing 4× per hour
No performance issues

✅ Audit logging complete

All events logged
Can query historical data
Manual operator action tracking works

✅ KPIs tracked

All 5 KPIs calculable
Monthly reports generated
Trends visible

✅ Production ready

Documentation complete
Monitoring setup
Runbook tested

Project Timeline

Waterfall Approach (Sequential)

Phase	Duration	Cumulative
Phase 1: Data Quality	2-3 weeks	2-3 weeks
Phase 2: Exit Strategy	2-3 weeks	4-6 weeks
Phase 3: Position Risk	1-2 weeks	5-8 weeks
Phase 4: Testing	1 week	6-9 weeks
Phase 5: Operational	1 week	7-10 weeks

Total: 7-10 weeks (1.5-2.5 months)

Agile Approach (Parallel where possible)

Sprint 1 (Week 1-2): Phase 1 + Start Phase 4 unit tests
Sprint 2 (Week 3-4): Phase 2 + Continue Phase 4
Sprint 3 (Week 5-6): Phase 3 + Complete Phase 4 (backtesting)
Sprint 4 (Week 7): Phase 5 (operational)

Total: 7 weeks (1.75 months) with parallel execution

Immediate Next Steps (Week 1)

Day 1-2: Setup & Planning

Review this SOW with stakeholder (Craig)
Set up development environment in .builders/0013-market-maker-mvp
Create feature branch: feature/phase-1-data-quality
Set up project tracking (GitHub issues/project board)

Day 3-5: Start Phase 1.1 (Metric Calculations)

Implement ADX calculation (src/regime/metrics/adx.py)
Implement Efficiency Ratio (src/regime/metrics/efficiency_ratio.py)
Unit tests for both (10 test cases)
Validate against TradingView/TA-Lib

Week 2: Continue Phase 1

Implement remaining metrics (autocorrelation, OU, slope, BB)
Complete all unit tests (22 test cases)
Extract metrics from regime analysis (modify engine.py)
Manual testing with real data

Week 3: Complete Phase 1

Appendices

A. File Structure After Completion

repos/market-making/metrics-service/
├── src/
│   ├── regime/
│   │   ├── metrics/              # NEW
│   │   │   ├── adx.py
│   │   │   ├── efficiency_ratio.py
│   │   │   ├── autocorrelation.py
│   │   │   ├── ou_process.py
│   │   │   ├── slope.py
│   │   │   └── bollinger.py
│   │   ├── validation/           # NEW
│   │   │   └── schema_validator.py
│   │   ├── quality/              # NEW
│   │   │   └── dashboard.py
│   │   └── engine.py             # MODIFIED (TODOs removed)
│   ├── exit_strategy/
│   │   ├── triggers/             # NEW
│   │   │   ├── mandatory.py
│   │   │   ├── latest_acceptable.py
│   │   │   └── warning.py
│   │   ├── evaluator.py          # ENHANCED
│   │   ├── state_tracker.py      # ENHANCED
│   │   ├── history_loader.py     # NEW
│   │   ├── audit_logger.py       # NEW
│   │   └── kpis.py               # NEW
│   ├── position/                 # NEW
│   │   ├── tracker.py
│   │   └── risk_calculator.py
│   └── ...
├── tests/
│   ├── regime/
│   │   └── metrics/              # NEW (22 test cases)
│   ├── exit_strategy/
│   │   └── triggers/             # NEW (26 test cases)
│   ├── position/                 # NEW
│   └── integration/              # ENHANCED
├── backtest/                     # NEW
│   └── regime_exit_backtest.py
├── config/
│   └── exit_strategy_config.yaml # NEW
└── docs/                         # NEW
    ├── ops/
    │   ├── runbook.md
    │   └── troubleshooting.md
    ├── metrics_guide.md
    └── configuration.md

B. Configuration Reference

exit_strategy_config.yaml (complete example):

# Exit Strategy Configuration
# Version: 1.0.0
 
evaluation:
  cadence_minutes: 15
  lookback_hours: 12
  history_cache_hours: 24
 
exit_rules:
  # LATEST_ACCEPTABLE_EXIT triggers
  latest_acceptable_exit:
    transition_persistence_4h_bars: 2
    transition_persistence_1h_bars: 4
    mean_reversion_halflife_multiplier: 2.0
    volatility_expansion_threshold: 1.25
    zscore_reversion_failure_bars: 6
    
  # WARNING triggers (require 2+ conditions)
  warning:
    transition_probability_threshold: 0.40
    regime_confidence_decline_bars: 3
    efficiency_ratio_threshold: 0.6
    volatility_expansion_min: 1.10
    volatility_expansion_max: 1.25
    
  # MANDATORY_EXIT triggers
  mandatory_exit:
    consecutive_closes_outside_range: 2
    directional_swing_bars: 6  # For HH/HL pattern
    stop_loss_buffer_atr: 0.1  # Trigger before actual stop
 
# Notification configuration
notifications:
  rate_limits:
    warning_min_hours: 4
    latest_acceptable_min_hours: 2
    mandatory_min_hours: 1
    
  pushover:
    enabled: true
    priority_map:
      WARNING: 0           # Normal priority
      LATEST_ACCEPTABLE_EXIT: 1  # High priority
      MANDATORY_EXIT: 2    # Emergency priority
      
  email:
    enabled: true
    from: grid-alerts@example.com
    to:
      - craig@example.com
    subject_prefix: "[Grid Exit]"
 
# Position risk configuration
position_risk:
  api_timeout_seconds: 10
  retry_attempts: 3
  retry_backoff_multiplier: 2.0
  
  profit_giveback_estimation:
    min_atr_multiplier: 0.5  # Slow drift scenario
    max_atr_multiplier: 1.0  # Acceleration scenario
 
# KPI targets
kpis:
  review_cadence_days: 30
  targets:
    exit_within_acceptable_window_pct: 90
    profit_retention_ratio: 0.75
    stop_loss_avoidance_rate: 0.95
    true_transition_detection_rate: 0.70
    mandatory_exit_compliance_pct: 100
 
# Baseline calculation
baselines:
  ou_halflife:
    calculation_window_days: 7
    regime_filter: RANGE_OK  # Only calculate during ranging
    min_samples: 48  # Minimum 48 hours of data
  
  volatility:
    calculation_window_days: 30
    percentile_for_expansion: 0.8  # 80th percentile

C. Metric Calculation Formulas

ADX (Average Directional Index):

1. Calculate +DM, -DM (Directional Movement)
2. Calculate +DI, -DI (Directional Indicators)
3. Calculate DX = |+DI - -DI| / (+DI + -DI) × 100
4. ADX = Smoothed average of DX (typically 14 periods)

Efficiency Ratio:

ER = |Price[0] - Price[n]| / Σ|Price[i] - Price[i-1]|

Where:
- Numerator = Net price change
- Denominator = Sum of absolute bar-to-bar changes
- Range: [0, 1]
- Higher = more efficient trend

Lag-1 Autocorrelation:

r = Σ[(x[i] - x̄)(x[i-1] - x̄)] / Σ(x[i] - x̄)²

Where:
- x̄ = mean of series
- Range: [-1, 1]
- Negative = mean reverting

OU Half-Life:

1. Fit AR(1) model: x[t] = ϕ × x[t-1] + ε
2. Half-life = -log(2) / log(ϕ)

Requirements:
- |ϕ| < 1 (stationary)
- If |ϕ| ≥ 1, reject (non-stationary)

Normalized Slope:

slope = (Price[0] - Price[n]) / n
normalized_slope = slope / ATR

Where:
- n = lookback period (bars)
- Positive = uptrend
- Negative = downtrend

Bollinger Band Bandwidth:

bandwidth = (upper_band - lower_band) / middle_band

Where:
- middle_band = 20-period SMA
- upper_band = SMA + (2 × stddev)
- lower_band = SMA - (2 × stddev)

Conclusion

This Statement of Work provides a comprehensive roadmap to bring the market-making system from 40% complete to production-ready MVP.

Critical Path:

✅ Phase 1 (Data Quality) - MUST complete first
✅ Phase 2 (Exit Strategy) - Core value proposition
✅ Phase 4 (Testing) - Cannot deploy without

Optional (defer if needed):

Phase 3 (Position Risk) - Enhances notifications but not blocking
Phase 5 (Operational) - Nice to have

Estimated Timeline: 7-10 weeks at 20-40h/week

Next Action: Start Phase 1.1 - Implement ADX calculation

Document Version: 1.0
Last Updated: 2026-01-31
Author: AI Code Review
Status: Ready for Approval

Techcle Wiki

Explorer

Implementation Plan

Market-Making System: Comprehensive Analysis & Statement of Work

Executive Summary

Table of Contents

Current State Analysis

What’s Working ✅

1. Regime Detection Engine

2. Infrastructure

3. Notification System (Partial)

4. Grid Configuration Management

Critical Gaps Identified

Gap 1: Grid Exit Strategy - NOT IMPLEMENTED (P0)

LATEST_ACCEPTABLE_EXIT Triggers (NOT IMPLEMENTED)

WARNING Triggers (NOT IMPLEMENTED)

State Transition Tracking (NOT IMPLEMENTED)

Historical Data Loading (NOT IMPLEMENTED)

Gap 2: Position Risk Quantification - MISSING (P0)

Position Tracking

Capital Risk Calculator

Gap 3: Evaluation Cadence - WRONG (P1)

Gap 4: Audit Logging - MISSING (P2)

Gap 5: Testing - NEARLY ZERO (P0)

Data Quality Issues (CRITICAL)

The Smoking Gun: Hardcoded Dummy Values

Impact Analysis

Missing Calculations

Statement of Work

Phase 1: Data Trust & Quality (CRITICAL - P0)

Tasks

1.1 Implement Missing Metric Calculations (20-30h)

1.2 Extract Metrics from Regime Analysis (8-12h)

1.3 Add Data Validation (8-12h)

1.4 Create Data Quality Dashboard (4-6h)

1.5 Unit Tests (8-12h)

Phase 2: Complete Grid Exit Strategy (HIGH PRIORITY - P0)

Tasks

2.1 LATEST_ACCEPTABLE_EXIT Triggers (8-12h)

2.2 WARNING Triggers (4-6h)

2.3 State Transition Tracking (4-6h)

2.4 Historical Data Loading (4-6h)

2.5 Integration & Testing (8-12h)

2.6 Configuration & Documentation (4-6h)

Phase 3: Position Risk Quantification (MEDIUM PRIORITY - P1)

Tasks

3.1 KuCoin Position Tracker (8-12h)

3.2 Capital Risk Calculator (6-8h)

3.3 Enhance Notifications (4-6h)

3.4 Error Handling & Graceful Degradation (4-6h)

3.5 Testing & Validation (8-12h)

Phase 4: Testing & Validation (CRITICAL - P0)

Tasks

4.1 Unit Tests for Metric Calculations (8-10h)

4.2 Unit Tests for Exit Triggers (10-12h)

4.3 Integration Tests (10-12h)

4.4 Backtesting Framework (12-16h)

4.5 CI/CD Integration (4-6h)

Phase 5: Operational Improvements (MEDIUM - P2)

Tasks

5.1 15-Minute Evaluation Cadence (0.5h)

5.2 Audit Logging (3-4h)

5.3 KPI Tracking (4-6h)

5.4 Documentation (4-6h)

5.5 Monitoring & Alerting (4-6h)

5.6 Performance Optimization (4-6h)

Risk Assessment

Technical Risks

Operational Risks

Schedule Risks

Success Criteria

Phase 1 (Data Quality) - Complete When:

Phase 2 (Exit Strategy) - Complete When:

Phase 3 (Position Risk) - Complete When:

Phase 4 (Testing) - Complete When:

Phase 5 (Operational) - Complete When:

Project Timeline

Waterfall Approach (Sequential)

Agile Approach (Parallel where possible)

Immediate Next Steps (Week 1)