# GitHub Issues for Investment Platform

This file contains all 47 issues to be created. Run the creation script or create manually.

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## Phase 1: Database Foundation

### Issue 1: Database setup - SQLAlchemy + PostgreSQL/SQLite
**Labels:** enhancement, database, priority-high

Create database/db.py with:
- SQLAlchemy engine configuration
- PostgreSQL for production, SQLite for development
- Session management (get_db, get_db_session)
- Connection pooling
- Environment variable configuration (DATABASE_URL)

**Acceptance Criteria:**
- Can connect to both PostgreSQL and SQLite
- Session management works correctly
- Environment variables properly loaded

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### Issue 2: User model - profiles, tax jurisdiction, API keys
**Labels:** enhancement, database, priority-high
**Depends on:** #1

Create database/models/user.py with:
- id, email, name, hashed_password
- tax_jurisdiction (AU, US, etc.)
- timezone (default: Australia/Sydney)
- api_key for programmatic access
- is_active, is_verified flags
- created_at, updated_at timestamps

**Acceptance Criteria:**
- Can create, read, update, delete users
- Tax jurisdiction defaults to AU

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### Issue 3: Portfolio model - live, paper, backtest types
**Labels:** enhancement, database, priority-high
**Depends on:** #1, #2

Create database/models/portfolio.py with:
- PortfolioType enum (live, paper, backtest)
- BrokerType enum (alpaca, ibkr, paper)
- initial_capital, current_cash, currency
- strategy_name, strategy_config (JSON)
- CGT tracking fields
- Relationship to User

**Acceptance Criteria:**
- Can create multiple portfolios per user
- Supports all three portfolio types

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### Issue 4: Settings model - risk profiles, alert preferences
**Labels:** enhancement, database, priority-high
**Depends on:** #1, #2

Create database/models/settings.py with:
- RiskProfile enum (conservative, moderate, aggressive)
- max_position_pct, max_daily_loss_pct, default_stop_loss_pct
- position_sizing_method (fixed_fractional, kelly, risk_parity)
- Alert preferences (email, slack, sms with contact info)
- Trading hours
- LLM preferences

**Acceptance Criteria:**
- One-to-one relationship with User
- All risk parameters have sensible defaults

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### Issue 5: Trade model - execution history with CGT tracking
**Labels:** enhancement, database, priority-high
**Depends on:** #1, #3

Create database/models/trade.py with:
- symbol, side (buy/sell), quantity, price, total_value
- order_type, status (pending, filled, cancelled)
- signal_source, signal_confidence
- CGT fields: acquisition_date, cost_basis_per_unit, cost_basis_total
- holding_period_days, cgt_discount_eligible (>12 months)
- cgt_gross_gain, cgt_gross_loss, cgt_net_gain
- tax_year (Australian FY July-June)
- fx_rate_to_aud for foreign assets

**Acceptance Criteria:**
- Full CGT calculation support
- Tax year correctly calculated (July-June)
- 50% discount eligibility tracked

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### Issue 6: Alembic migrations setup
**Labels:** enhancement, database, priority-high
**Depends on:** #1-5

Setup Alembic for database migrations:
- Initialize Alembic configuration
- Create initial migration for all models
- Add upgrade/downgrade scripts
- Document migration workflow in README

**Acceptance Criteria:**
- Can run migrations up and down
- Initial migration creates all tables

---

## Phase 2: Data Layer

### Issue 7: FRED API integration - interest rates, M2, GDP, CPI
**Labels:** enhancement, data, priority-high

Create spektiv/dataflows/fred.py with:
- FRED API client (fredapi package)
- Series: DFF (Fed Funds), DGS10 (10Y Treasury), M2SL (M2), GDP, CPIAUCSL
- VIX from CBOE
- Date range filtering
- Error handling and retries

**Acceptance Criteria:**
- Can fetch all specified series
- Proper date formatting
- Rate limit handling

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### Issue 8: Multi-timeframe aggregation - weekly/monthly OHLCV
**Labels:** enhancement, data, priority-high

Create spektiv/dataflows/multi_timeframe.py with:
- Aggregate daily OHLCV to weekly
- Aggregate daily OHLCV to monthly
- Preserve volume correctly
- Handle partial periods

**Acceptance Criteria:**
- Weekly aggregation (Mon-Fri)
- Monthly aggregation
- Works with yfinance data

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### Issue 9: Benchmark data - SPY, sector ETFs
**Labels:** enhancement, data, priority-high

Create spektiv/dataflows/benchmark.py with:
- SPY for broad market
- Sector ETFs (XLF, XLK, XLE, XLV, etc.)
- Relative strength calculation
- Correlation calculation

**Acceptance Criteria:**
- Can calculate relative strength vs SPY
- Can calculate rolling correlations

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### Issue 10: Interface routing - add new data vendors
**Labels:** enhancement, data, priority-high
**Depends on:** #7-9

Update spektiv/dataflows/interface.py:
- Add FRED to VENDOR_METHODS
- Add multi_timeframe routing
- Add benchmark routing
- Update TOOLS_CATEGORIES

**Acceptance Criteria:**
- New vendors accessible via route_to_vendor
- Fallback chains work correctly

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### Issue 11: Data caching layer - FRED rate limits
**Labels:** enhancement, data, priority-medium
**Depends on:** #7

Add caching for FRED data:
- File-based cache for FRED responses
- Cache invalidation strategy (daily for most series)
- Memory cache for frequently accessed data

**Acceptance Criteria:**
- Reduces API calls
- Cache respects rate limits

---

## Phase 3: New Analysts

### Issue 12: Momentum Analyst - multi-TF momentum, ROC, ADX
**Labels:** enhancement, agents, priority-high
**Depends on:** #8

Create spektiv/agents/analysts/momentum_analyst.py with:
- Multi-timeframe momentum (daily, weekly, monthly)
- Rate of Change (ROC) calculation
- ADX (Average Directional Index)
- Relative strength vs benchmark
- Volume-weighted momentum

**Acceptance Criteria:**
- Produces structured report like other analysts
- Integrates with debate workflow

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### Issue 13: Macro Analyst - FRED interpretation, regime detection
**Labels:** enhancement, agents, priority-high
**Depends on:** #7

Create spektiv/agents/analysts/macro_analyst.py with:
- Interpret FRED data for market regime
- Interest rate environment (rising/falling/stable)
- Inflation/deflation signals
- Risk-on/risk-off assessment
- Economic cycle positioning

**Acceptance Criteria:**
- Produces structured macro report
- Identifies current market regime

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### Issue 14: Correlation Analyst - cross-asset, sector rotation
**Labels:** enhancement, agents, priority-high
**Depends on:** #9

Create spektiv/agents/analysts/correlation_analyst.py with:
- Cross-asset correlation analysis
- Sector rotation signals
- Safe haven flows (gold, bonds)
- Currency correlations (if applicable)
- Divergence detection

**Acceptance Criteria:**
- Produces correlation report
- Identifies unusual correlations

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### Issue 15: Position Sizing Manager - Kelly, risk parity, ATR
**Labels:** enhancement, agents, priority-high

Create spektiv/agents/managers/position_sizing_manager.py with:
- Kelly criterion calculation
- Risk parity sizing
- Fixed fractional sizing
- ATR-based sizing
- Maximum position limits

**Acceptance Criteria:**
- Given signal and confidence, outputs position size
- Respects risk limits from settings

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### Issue 16: Analyst integration - add to graph/setup.py workflow
**Labels:** enhancement, agents, priority-high
**Depends on:** #12-15

Update spektiv/graph/setup.py:
- Add new analysts to analyst team
- Update debate workflow to include new insights
- Ensure position sizing manager is called

**Acceptance Criteria:**
- All new analysts contribute to analysis
- Backward compatible with existing workflow

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## Phase 4: Memory System

### Issue 17: Layered memory - recency, relevancy, importance scoring
**Labels:** enhancement, memory, priority-medium
**Depends on:** #5

Create spektiv/memory/layered_memory.py with:
- Recency scoring (exponential decay)
- Relevancy scoring (similarity to current situation)
- Importance scoring (based on P&L impact)
- Memory retrieval with composite score

**Acceptance Criteria:**
- FinMem pattern implemented
- Can retrieve top-k relevant memories

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### Issue 18: Trade history memory - outcomes, agent reasoning
**Labels:** enhancement, memory, priority-medium
**Depends on:** #5, #17

Create spektiv/memory/trade_history.py with:
- Store trade outcomes with full context
- Link to agent reasoning at time of trade
- Track what worked vs what didn't
- Pattern recognition for similar setups

**Acceptance Criteria:**
- Full trade context preserved
- Can query by symbol, timeframe, outcome

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### Issue 19: Risk profiles memory - user preferences over time
**Labels:** enhancement, memory, priority-medium
**Depends on:** #4, #17

Create spektiv/memory/risk_profiles.py with:
- User risk preferences over time
- Portfolio behavior patterns
- Drawdown tolerance history
- Position sizing history

**Acceptance Criteria:**
- Tracks risk behavior evolution
- Informs position sizing

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### Issue 20: Memory integration - retrieval in agent prompts
**Labels:** enhancement, memory, priority-medium
**Depends on:** #17-19

Integrate memory into agents:
- Add memory retrieval to analyst prompts
- Include relevant past trades in context
- Update trader agent with memory

**Acceptance Criteria:**
- Agents reference relevant past trades
- Memory influences recommendations

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## Phase 5: Execution Layer

### Issue 21: Broker base interface - abstract broker class
**Labels:** enhancement, execution, priority-high

Create execution/brokers/base.py with:
- Abstract Broker class
- Methods: connect, disconnect, submit_order, cancel_order
- Methods: get_positions, get_account, get_order_status
- Error handling patterns

**Acceptance Criteria:**
- Clear interface contract
- All brokers implement same interface

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### Issue 22: Broker router - route by asset class
**Labels:** enhancement, execution, priority-high
**Depends on:** #21

Create execution/brokers/broker_router.py with:
- Route by exchange (NYSE, NASDAQ -> Alpaca)
- Route by asset type (futures -> IBKR)
- Route by symbol suffix (.AX -> IBKR)
- Fallback handling

**Acceptance Criteria:**
- Correct routing for all asset classes
- Clear routing rules

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### Issue 23: Alpaca broker - US stocks, ETFs, crypto
**Labels:** enhancement, execution, priority-high
**Depends on:** #21, #22

Create execution/brokers/alpaca_broker.py with:
- Alpaca API integration (alpaca-py)
- Paper and live modes
- US stocks, ETFs
- Crypto trading
- Order submission and tracking

**Acceptance Criteria:**
- Can place orders via Alpaca API
- Supports paper trading mode

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### Issue 24: IBKR broker - futures, ASX equities
**Labels:** enhancement, execution, priority-high
**Depends on:** #21, #22

Create execution/brokers/ibkr_broker.py with:
- Interactive Brokers API (ib_insync)
- Futures contracts (GC, SI, ES)
- Australian equities (ASX)
- Order submission and tracking

**Acceptance Criteria:**
- Can place orders via IBKR
- Supports futures and ASX

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### Issue 25: Paper broker - simulation mode
**Labels:** enhancement, execution, priority-high
**Depends on:** #21, #22

Create execution/brokers/paper_broker.py with:
- Simulated order execution
- Realistic fill simulation
- Position tracking
- P&L calculation
- No real money at risk

**Acceptance Criteria:**
- Full trading simulation
- Tracks positions and P&L

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### Issue 26: Order types and manager - market, limit, stop, trailing
**Labels:** enhancement, execution, priority-high
**Depends on:** #21

Create execution/orders/:
- order_types.py - Order, OrderType, OrderStatus enums
- order_manager.py - Order lifecycle management
- Support: market, limit, stop, stop_limit, trailing_stop

**Acceptance Criteria:**
- All order types supported
- Order state machine correct

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### Issue 27: Risk controls - position limits, loss limits
**Labels:** enhancement, execution, priority-high
**Depends on:** #4

Create execution/risk_controls/:
- position_limits.py - Max position size, concentration
- loss_limits.py - Daily loss limit, drawdown limit
- Pre-trade validation

**Acceptance Criteria:**
- Orders rejected if limits exceeded
- Clear rejection messages

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## Phase 6: Portfolio Management

### Issue 28: Portfolio state - holdings, cash, mark-to-market
**Labels:** enhancement, portfolio, priority-high
**Depends on:** #3, #5

Create portfolio/portfolio_state.py with:
- Current holdings
- Cash balance
- Total portfolio value (mark-to-market)
- Real-time pricing

**Acceptance Criteria:**
- Accurate portfolio valuation
- Handles multiple currencies

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### Issue 29: Position tracker - open/closed, cost basis, tax lots
**Labels:** enhancement, portfolio, priority-high
**Depends on:** #5, #28

Create portfolio/position_tracker.py with:
- Open positions with cost basis
- Closed positions with realized P&L
- Tax lot tracking (FIFO, LIFO, specific ID)
- Average cost calculation

**Acceptance Criteria:**
- Correct cost basis tracking
- Tax lot matching works

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### Issue 30: Performance metrics - Sharpe, drawdown, returns
**Labels:** enhancement, portfolio, priority-high
**Depends on:** #28, #29

Create portfolio/performance.py with:
- Daily, monthly, yearly returns
- Sharpe ratio
- Maximum drawdown
- Win rate, profit factor
- Benchmark comparison

**Acceptance Criteria:**
- Industry-standard calculations
- Matches known benchmarks

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### Issue 31: Australian CGT calculator - 50% discount, tax reports
**Labels:** enhancement, portfolio, priority-high
**Depends on:** #5, #29

Create portfolio/tax_calculator.py with:
- Australian CGT calculations
- 50% discount for assets held >12 months
- Tax year reports (July-June)
- Currency conversion for foreign assets
- Capital loss tracking

**Acceptance Criteria:**
- Correct CGT calculations
- Tax year correctly determined
- Report format suitable for tax return

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## Phase 7: Simulation & Strategy

### Issue 32: Scenario runner - parallel portfolio simulations
**Labels:** enhancement, simulation, priority-high
**Depends on:** #25, #28

Create simulation/scenario_runner.py with:
- Run multiple portfolios in parallel
- Same market data, different strategies
- Paper trading infrastructure
- Result collection

**Acceptance Criteria:**
- Can run 5+ parallel simulations
- Results properly isolated

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### Issue 33: Strategy comparator - performance comparison, stats
**Labels:** enhancement, simulation, priority-high
**Depends on:** #30, #32

Create simulation/strategy_comparator.py with:
- Compare performance across scenarios
- Statistical significance testing
- Risk-adjusted return comparison
- Ranking and scoring

**Acceptance Criteria:**
- Clear comparison output
- Statistical confidence levels

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### Issue 34: Economic conditions - regime tagging, evaluation
**Labels:** enhancement, simulation, priority-high
**Depends on:** #7, #32

Create simulation/economic_conditions.py with:
- Tag scenarios by economic regime
- Bull/bear/sideways market detection
- Evaluate strategy performance by condition
- Regime-specific recommendations

**Acceptance Criteria:**
- Correct regime identification
- Performance breakdown by regime

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### Issue 35: Signal to order converter
**Labels:** enhancement, strategy, priority-high
**Depends on:** #26

Create strategy/signal_to_order.py with:
- Convert BUY/SELL signals to orders
- Apply position sizing
- Set stop loss and take profit
- Order validation

**Acceptance Criteria:**
- Signals converted to valid orders
- Risk parameters applied

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### Issue 36: Strategy executor - end-to-end orchestration
**Labels:** enhancement, strategy, priority-high
**Depends on:** #32-35

Create strategy/strategy_executor.py with:
- End-to-end orchestration
- Signal generation -> Order -> Execution
- Error handling and retries
- Logging and monitoring

**Acceptance Criteria:**
- Full trade lifecycle managed
- Robust error handling

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## Phase 8: Alerts

### Issue 37: Alert manager - orchestration and routing
**Labels:** enhancement, alerts, priority-medium
**Depends on:** #4

Create alerts/alert_manager.py with:
- Alert orchestration
- Route to appropriate channels
- Priority levels (info, warning, critical)
- Throttling to prevent spam

**Acceptance Criteria:**
- Alerts routed correctly
- Critical alerts always delivered

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### Issue 38: Email channel - SMTP/SendGrid
**Labels:** enhancement, alerts, priority-medium
**Depends on:** #37

Create alerts/channels/email_channel.py with:
- SMTP support
- SendGrid API support
- HTML email templates
- Delivery confirmation

**Acceptance Criteria:**
- Emails delivered reliably
- Professional formatting

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### Issue 39: Slack channel - webhooks
**Labels:** enhancement, alerts, priority-medium
**Depends on:** #37

Create alerts/channels/slack_channel.py with:
- Slack webhook integration
- Rich message formatting
- Channel routing

**Acceptance Criteria:**
- Messages appear in Slack
- Formatting correct

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### Issue 40: SMS channel - Twilio
**Labels:** enhancement, alerts, priority-medium
**Depends on:** #37

Create alerts/channels/sms_channel.py with:
- Twilio API integration
- SMS formatting
- Delivery status tracking

**Acceptance Criteria:**
- SMS delivered
- Critical alerts work

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## Phase 9: Backtest

### Issue 41: Backtest engine - historical replay, slippage
**Labels:** enhancement, backtest, priority-medium
**Depends on:** #25, #28

Create backtest/backtest_engine.py with:
- Historical data replay
- Slippage modeling
- Commission modeling
- Position sizing simulation

**Acceptance Criteria:**
- Realistic backtesting
- Configurable slippage/commission

---

### Issue 42: Results analyzer - metrics, trade analysis
**Labels:** enhancement, backtest, priority-medium
**Depends on:** #30, #41

Create backtest/results_analyzer.py with:
- Performance metrics
- Trade-by-trade analysis
- Equity curve
- Drawdown analysis

**Acceptance Criteria:**
- Comprehensive analysis
- Visual outputs

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### Issue 43: Report generator - PDF/HTML reports
**Labels:** enhancement, backtest, priority-low
**Depends on:** #42

Create backtest/report_generator.py with:
- PDF report generation
- HTML report generation
- Charts and graphs
- Summary statistics

**Acceptance Criteria:**
- Professional reports
- Exportable

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## Phase 10: API & Docs

### Issue 44: FastAPI application setup
**Labels:** enhancement, api, priority-low
**Depends on:** #1-6

Create api/app.py with:
- FastAPI application
- CORS configuration
- Error handling
- Health check endpoint

**Acceptance Criteria:**
- API starts and responds
- Health check works

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### Issue 45: API routes - users, portfolios, trades, signals
**Labels:** enhancement, api, priority-low
**Depends on:** #44

Create api/routes/:
- users.py - User CRUD
- portfolios.py - Portfolio CRUD
- trades.py - Trade history
- signals.py - Signal retrieval

**Acceptance Criteria:**
- All CRUD operations work
- Proper error responses

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### Issue 46: API authentication - JWT
**Labels:** enhancement, api, priority-low
**Depends on:** #44, #45

Add JWT authentication:
- Login endpoint
- Token generation
- Token validation middleware
- Refresh tokens

**Acceptance Criteria:**
- Secure authentication
- Token refresh works

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### Issue 47: Documentation - user guide, developer docs
**Labels:** documentation, priority-low

Create documentation:
- User guide (how to use)
- Developer guide (how to extend)
- API documentation (OpenAPI)
- Architecture overview

**Acceptance Criteria:**
- Clear documentation
- Getting started guide