portfolio-manager - SKILL.md Agent Skill

name: portfolio-manager description: Portfolio Manager — BerkahKarya Quant Fund. Use when relevant to this domain. domain: trading tags:

algorithms
manager
markets
portfolio
trading

Portfolio Manager — BerkahKarya Quant Fund

Managing capital across strategies. Risk first, returns second.

Current Trading Setup

Account: Paper trading (transitioning to live)
Broker:  Ostium (cTrader platform)
Pairs:   XAUUSD primary, future: EURUSD, GBPUSD
Capital: $100 starting (phase 1)
Target:  $528/month = 528% ROI (aggressive, paper verified first)

Strategy Portfolio

Active, planned, and future strategies ranked by readiness and expected return.

Strategy 1: XAUUSD Asia 7-Candle Breakout (Active)

Session:    Asia, 07:00-15:00 UTC+7
Entry:      15:00 UTC+7, breakout of 7-candle range
Win rate:   61.4% (backtested)
Profit PF:  4.1
Risk/trade: 1% account ($1 paper, $10 live phase 1)
Target:     $528/month
Status:     ✅ Strategy ready, paper trading pending

Strategy 2: XAUUSD London Session (Planned)

Session:    London open 14:00 UTC+7
Type:       Trend following on breakout of Asian range
Status:     📋 Research phase

Strategy 3: Multi-pair Momentum (Future)

Pairs:      EURUSD, GBPUSD, USDJPY
Type:       High timeframe trend + low timeframe entry
Status:     📋 Concept phase

Risk Management Framework

Position sizing formulas, maximum drawdown rules, and trade logging format for the quant fund.

Position Sizing

def calculate_position_size(account_balance, risk_pct=0.01, stop_pips=20):
    """
    Standard position sizing with 1% risk per trade.
    
    account_balance: USD
    risk_pct: 0.01 = 1% risk
    stop_pips: pips to stop loss
    
    Returns: lot size
    """
    risk_amount = account_balance * risk_pct
    pip_value = 10  # $10/pip for 1 standard lot XAUUSD
    lot_size = risk_amount / (stop_pips * pip_value)
    return round(lot_size, 2)

# Example: $1000 account, 1% risk, 20 pip stop
# lot_size = (1000 * 0.01) / (20 * 10) = $10 / $200 = 0.05 lots

Maximum Drawdown Rules

Daily DD limit:    3% → STOP trading for the day
Weekly DD limit:   7% → STOP, review strategy
Monthly DD limit:  15% → STOP, report to Paijo, strategy review
Account DD limit:  25% → FULL STOP, return to paper trading

Trade Logging

TRADE_LOG_PATH = ".vilona/knowledge/trading/trading_log.json"

trade_entry = {
    "id": "trade_001",
    "date": "2026-03-13",
    "time": "15:02",
    "pair": "XAUUSD",
    "direction": "BUY",
    "entry": 2650.50,
    "stop_loss": 2649.50,   # 10 pip stop
    "take_profit": 2652.50,  # 20 pip TP (1:2 RR)
    "lot_size": 0.01,
    "risk_usd": 1.00,
    "strategy": "asia_7c_breakout",
    "session": "asia",
    "candle_range_pips": 8.5,
    "outcome": "WIN",
    "pnl_usd": 2.00,
    "duration_min": 45,
    "notes": "Clean breakout, held to TP"
}

Performance Analytics

Monthly performance reports and phase gate criteria for progression from paper to live to scaled trading.

Monthly Performance Report

╔══════════════════════════════════════════╗
║     QUANT FUND MONTHLY REPORT            ║
║     {month} {year}                       ║
╠══════════════════════════════════════════╣
║ Starting Balance: ${start}               ║
║ Ending Balance:   ${end}                 ║
║ Net P&L:          ${pnl} ({pct}%)       ║
╠══════════════════════════════════════════╣
║ TRADE STATS                              ║
║ Total trades:    {total}                 ║
║ Win rate:        {wr}%                   ║
║ Profit factor:   {pf}                    ║
║ Avg win:         ${avg_win}              ║
║ Avg loss:        ${avg_loss}             ║
║ Best trade:      ${best}                 ║
║ Worst trade:     ${worst}                ║
╠══════════════════════════════════════════╣
║ RISK METRICS                             ║
║ Max daily DD:    {max_dd}%               ║
║ Sharpe ratio:    {sharpe}                ║
║ Recovery factor: {rf}                    ║
╚══════════════════════════════════════════╝

Phase Gates (Paper → Live → Scale)

Phase 1: Paper Trading
  □ 30 trades completed
  □ Win rate ≥55% over 30 trades
  □ Max DD <15% of paper account
  □ Profit factor ≥1.5
  → PASS: Move to Phase 2

Phase 2: Live Trading ($100)
  □ 30 live trades
  □ Win rate ≥55%
  □ Profitable month (net positive)
  □ Following risk rules strictly
  → PASS: Double capital ($200)

Phase 3: Scaling
  □ 3 consecutive profitable months
  □ Each month: double previous capital
  □ Max $10,000 per strategy
  
Phase 4: Quant Fund
  □ $50K+ AUM
  □ External investors
  □ Monthly investor reports
  □ Audited track record

Investor Reporting (Future)

When BerkahKarya Quant Fund accepts external capital:

Monthly P&L report (this template)
Quarterly strategy review
Risk disclosure document
Track record (audited)
Monthly Sharpe ratio, drawdown stats

When to Use

Managing capital allocation across multiple trading strategies
Tracking performance metrics (IRR, MOIC, Sharpe, win rate) for a quant fund
Implementing phase gates for progression from paper to live to scaled trading
Calculating position sizes with risk-based formulas
Generating investor reports for fund performance review

Red Flags

Position sizing exceeds 1% risk per trade without override justification
Drawdown exceeds daily 3% limit without automatic trading halt
Win rate below 55% after 30+ trades (strategy underperforming)
Moving from paper to live without meeting all phase gate criteria
No trade log maintained (missing audit trail for performance review)

Verification

After completing portfolio management setup, confirm:

Position sizing formula tested with known account balances and risk percentages
Drawdown limits configured: daily 3%, weekly 7%, monthly 15%, account 25%
Trade log format captures all required fields (entry, SL, TP, P&L, strategy)
Phase gate criteria documented and measurable (30 trades, 55% WR, PF 1.5)
Monthly report template generates correct P&L, Sharpe, and drawdown stats

Integration

portfolio-manager → trading/strategy/xauusd_asia_7c_breakout (signals)
                 → finance-tracker (P&L reporting)
                 → telegram-userbot (trade alerts, daily P&L)
                 → business-intelligence (quant fund KPIs)

Overview

Section content — see SKILL.md body for full details.

Process

Analyze the task requirements
Apply domain expertise
Verify output quality