To address your request, I’ll create a series of matrix tables to analyze the impact of swing trading with a ₹300,000 portfolio, risking a maximum of 0.5% per trade (₹1,500), while targeting 8-40% price moves as discussed previously. The tables will cover different expectancies, win/loss ratios, returns, and their impact on the portfolio, using the chart patterns (flags, pennants, cup and handle, all-time highs, strong stocks from strong sectors) for momentum trading with 1-5 day holds. The goal is to grow the portfolio to ₹1 crore in 3 years, with strong risk management. I’ll include matrices for expectancy, single trade impact, portfolio growth over multiple trades, and drawdown risks, incorporating various win rates and risk-reward scenarios.
1. Key Assumptions
Initial Portfolio: ₹300,000
Risk per Trade: 0.5% (₹1,500)
Price Moves (Returns): 8%, 20%, 40% (based on capturing breakout moves)
Stop-Loss: 4%, 5%, 8% (aligned with pattern invalidation points)
Win Rates: 50%, 60%, 70% (based on pattern success rates of 60-80%, adjusted for execution)
Risk-Reward Ratios:
8% gain, 4% stop: 1:2
20% gain, 5% stop: 1:4
40% gain, 8% stop: 1:5
Expectancy Formula:
\text{Expectancy} = (\text{Win Rate} \times \text{Average Win}) - (\text{Loss Rate} \times \text{Average Loss})
Goal: Grow to ₹10,000,000 (33.33x) in 3 years (~147 trades/year, 12/month)
2. Expectancy Matrix
This matrix calculates expectancy as a percentage of the portfolio for different win rates and risk-reward ratios, with 0.5% risk per trade.
Table 1: Expectancy per Trade (%)
| | |
|---|
| | (0.50 × 8%) - (0.50 × 4%) = 2.0% (₹6,000) |
| | (0.50 × 20%) - (0.50 × 5%) = 7.5% (₹22,500) |
| | (0.50 × 40%) - (0.50 × 8%) = 16.0% (₹48,000) |
| | (0.60 × 8%) - (0.40 × 4%) = 3.2% (₹9,600) |
| | (0.60 × 20%) - (0.40 × 5%) = 10.0% (₹30,000) |
| | (0.60 × 40%) - (0.40 × 8%) = 20.8% (₹62,400) |
| | (0.70 × 8%) - (0.30 × 4%) = 4.4% (₹13,200) |
| | (0.70 × 20%) - (0.30 × 5%) = 12.5% (₹37,500) |
| | (0.70 × 40%) - (0.30 × 8%) = 25.6% (₹76,800) |
Interpretation:
At 60% win rate and 1:4 ratio (20% gain, 5% stop), expectancy is 10.0% (₹30,000 per trade).
For 40% gains (1:5, 60% win), expectancy is 20.8% (₹62,400), but such large moves are less frequent.
Higher win rates (70%) and risk-reward ratios (1:5) maximize expectancy, supporting aggressive growth.
3. Single Trade Impact on Portfolio
This matrix shows the effect of a single trade on the ₹300,000 portfolio, with 0.5% risk, across different returns and stop-losses.
Table 2: Single Trade Impact
Position Size Calculation:
Position size = Risk / Stop-Loss %. E.g., ₹1,500 / 0.05 = ₹30,000 for 5% stop.
Gains/losses are proportional to position size and price move.
Interpretation:
A 20% gain with 5% stop (1:4) yields ₹6,000 (2% portfolio gain), with a ₹1,500 loss (0.5%).
Larger moves (40%) increase profits (₹7,500, 2.5%) but require smaller positions due to wider stops (8%).
4. Portfolio Growth Over Multiple Trades
This matrix projects portfolio growth after 10, 25, and 50 trades, assuming compounding, for different expectancy scenarios.
Table 3: Portfolio Growth Over Trades
Calculation:
Portfolio after ( T ) trades = ₹300,000 × (1 + Expectancy)^T.
E.g., for 10.0% expectancy, after 50 trades: ₹300,000 × (1.10)^50 ≈ ₹35,172,585.
Interpretation:
At 10.0% expectancy (60% win, 1:4), 50 trades yield ₹35,172,585, exceeding ₹1 crore.
At 3.2% expectancy (60% win, 1:2), 50 trades yield ₹1,465,884, requiring more trades.
High expectancy (20.8%) produces extreme growth but assumes consistent 40% moves.
5. Trades Needed to Reach ₹1 Crore
To achieve 33.33x growth (₹300,000 to ₹10,000,000):
Expectancy 2.0% (50% win, 1:2): ~171 trades
1.020^T = 33.33 \implies T \approx 171
Expectancy 3.2% (60% win, 1:2): ~90 trades
1.032^T = 33.33 \implies T \approx 90
Expectancy 7.5% (50% win, 1:4): ~45 trades
1.075^T = 33.33 \implies T \approx 45
Expectancy 10.0% (60% win, 1:4): ~37 trades
1.100^T = 33.33 \implies T \approx 36.5
Expectancy 16.0% (50% win, 1:5): ~25 trades
1.160^T = 33.33 \implies T \approx 25
Expectancy 20.8% (60% win, 1:5): ~20 trades
1.208^T = 33.33 \implies T \approx 19.5
With 12 trades/month (147/year), you could reach ₹1 crore in:
~7.5 months (90 trades) at 3.2% expectancy.
~3 months (37 trades) at 10.0% expectancy.
~1.5 months (20 trades) at 20.8% expectancy.
6. Drawdown Risk Matrix
This matrix shows the impact of consecutive losses with 0.5% risk per trade.
Table 4: Drawdown After Consecutive Losses
Calculation:
Portfolio after ( n ) losses = ₹300,000 × (0.995)^n.
E.g., after 10 losses: ₹300,000 × (0.995)^10 ≈ ₹285,657.
Interpretation:
Risking 0.5% keeps drawdowns low (4.78% after 10 losses, 9.59% after 20).
This supports aggressive growth with minimal capital erosion.
7. Risk Management Recommendations
Risk 0.5% per Trade: ₹1,500 risk ensures low drawdowns (e.g., 4.78% after 10 losses).
Position Sizing: Adjust based on stop-loss (e.g., ₹1,500 / 0.05 = ₹30,000 for 20% gain, 5% stop).
Target 20%+ Moves: Prioritize 1:4 or 1:5 ratios (20-40% gains, 5-8% stops) for 10.0-20.8% expectancy.
Win Rate: Aim for 60%+ by selecting high-probability setups (e.g., flags in strong sectors, all-time highs).
Stop-Losses: Place 4-8% below entry, based on pattern invalidation.
Trade Frequency: Execute 12 trades/month, holding 1-3 positions (1-5 days each).
Drawdown Cap: Pause trading if drawdown exceeds 10% to reassess strategy.
8. Example Trade Scenario
Setup: Cup and handle breakout, stock at ₹100, stop-loss at ₹95 (5%), target at ₹120 (20%, 1:4 ratio).
Risk: 0.5% (₹1,500)
Position Size: ₹1,500 / 0.05 = ₹30,000 (300 shares)
Outcome:
Win: ₹120 × 300 = ₹36,000; Profit = ₹6,000 (2%); Portfolio = ₹306,000
Loss: ₹95 × 300 = ₹28,500; Loss = ₹1,500 (0.5%); Portfolio = ₹298,500
Expectancy (60% win): (0.60 × 20%) - (0.40 × 5%) = 10.0% (₹30,000)
Conclusion
Risking 0.5% per trade (₹1,500) with 8-40% price moves yields expectancies of 2.0-25.6% per trade, depending on win rate (50-70%) and risk-reward (1:2 to 1:5). A 60% win rate with a 1:4 ratio (20% gain, 5% stop) gives a 10.0% expectancy, requiring 37 trades to reach ₹1 crore (3 months at 12 trades/month). Drawdowns are minimal (4.78% after 10 losses), supporting aggressive growth with strong risk management. Focus on high-probability setups, maintain strict stop-losses, and backtest to ensure consistent 60%+ win rates.
Disclaimer: Grok is not a financial adviser; please consult one. Don't share information that can identify you.
