All Market Conditions
| Strategy Type | Performance Measurement and Analysis |
| Market Outlook | All Market Conditions |
| Risk Level | Analytical Tool - No Direct Risk |
| Time Horizon | Historical Analysis with Forward Application |
| Best Conditions | Sufficient trade sample size (30+ trades) |
| Avoid When | Too few trades for statistical significance |
| Trading Context | Win rates 45-55% common for scalping • Win rates 40-50% with larger R-multiples • Win rates 35-45% acceptable with trend following • Win rates 70-85% but with tail risk (the ASX ETO market is thinner than the underlying equities) |
| Market Characteristics | ~25-30% strong trend days • ~50% of trading days • ~20% high volatility days • Third-Thursday monthly expiry (XJO index options, ETOs) and the quarterly SPI 200 futures roll (Mar/Jun/Sep/Dec) affect those periods; there is no weekly expiry cycle |
| Cost Considerations | No securities transaction tax in Australia (no STT-equivalent) - a structural cost advantage, so net win rate sits closer to gross than in transaction-tax markets • Flat fee (e.g. ~$5-$10 with discount brokers) vs percentage (full-service) materially affects small trades • Wider in small/mid-caps and less liquid ETOs; tight in SPI 200 and ASX 20 names • 10% GST applies to brokerage/service fees (usually already included in the quoted brokerage) |
| Regulatory Notes | ASX-traded derivatives (ETOs, SPI 200 futures) are cleared and reported via ASX Clear; CFDs are OTC and subject to ASIC product-intervention limits on retail leverage • Share traders (carrying on a business): profits taxed as ordinary income at marginal rates. Investors: CGT applies, with a 50% discount on assets held >12 months. Speculative derivatives are generally on revenue account • ATO scrutiny rises with trader-vs-investor classification, trade frequency, and business-like conduct; substantiation of records is required • Maintain records for at least 5 years (ATO requirement) |
There's no universal 'good' win rate - it depends on your payoff ratio. A 35% win rate can be excellent with 3:1 payoff. A 70% win rate can be terrible with 1:4 payoff. Focus on positive expectancy, not win rate alone. That said, most successful traders have win rates between 35-65%. Extremely high (>80%) or low (<30%) win rates are rare and may indicate issues.
Use NET win rate (after all transaction costs) for realistic performance assessment. Gross win rate (before costs) can be misleading, especially for frequent traders or those trading illiquid instruments. The difference can be significant - a trade with a small gross profit might be a net loss after brokerage and the bid-ask spread. Australia has no securities transaction tax, so brokerage and spread are the main drags.
Minimum 30 trades for basic analysis, 100+ trades for reasonable confidence, 500+ trades for statistical significance of small edges. With only 20 trades, a 60% win rate could easily be luck. With 500 trades, a 55% win rate is likely real. More trades = more confidence in your win rate estimate.
One month is usually too short to draw conclusions. Win rate naturally varies due to market conditions and randomness. Before changing strategy, consider: How does this month compare to long-term average? Has market regime changed? Is sample size sufficient? Check at least 3 months of data before making changes.
Common reasons: 1) Slippage not fully accounted in backtest. 2) Execution delays in live trading. 3) Emotional decisions deviating from system. 4) Market impact when trading real size. 5) Overfitting in backtest design. Expect 5-15% reduction from backtest to live. This is normal.
Use binomial test comparing your win rate to 50% (no edge baseline). Calculate p-value = P(observed or more extreme | true rate = 50%). If p-value < 0.05, your win rate is statistically significant. Online calculators or scipy.stats.binom_test() can compute this. Remember: more trades = easier to achieve significance.
This is valuable information! Calculate conditional win rates for different regimes (trending/ranging, high/low volatility, etc.). Build trading rules: trade full size in high-WR conditions, reduced size or skip trades in low-WR conditions. This adaptive approach can significantly improve overall performance.
Monitor rolling win rate (30-100 trades). Compare recent vs historical performance. Use statistical tests (Chow test) for structural breaks. Warning signs: steady multi-month decline, recent WR significantly below baseline, increased variance. If decay detected, investigate cause (market change, competition, overtrading) and adapt.
Lower win rate = higher variance = more potential for losing streaks = higher risk of ruin if sizing is aggressive. A 40% WR strategy needs smaller position sizes than 60% WR to achieve same risk of ruin. Use Kelly criterion or Monte Carlo simulation to determine safe position sizing for your specific win rate and payoff ratio.
Yes, for detecting changes quickly. Exponentially weighted moving average (EWMA) of trade outcomes gives more weight to recent trades. This is more responsive to regime changes and edge decay. However, also maintain long-term average for baseline comparison. Both perspectives are valuable.
Use Beta-Binomial model. Prior: Beta(α₀, β₀) where α₀/(α₀+β₀) is prior mean. After k wins in n trades, posterior is Beta(α₀+k, β₀+n-k). Posterior mean shrinks toward prior. Use informative prior (e.g., Beta(10,10) for skepticism) to avoid overconfidence in small samples. Posterior gives full distribution, not just point estimate.
Features: market conditions (A-VIX, trend), technicals (RSI, volume), timing (day, hour), strategy-specific. Use logistic regression or gradient boosting (XGBoost). Critical: time-series train/test split (not random), proper calibration (Platt scaling), validation of probability estimates. Use predicted probability for position sizing and trade selection.
Uncorrelated trades: portfolio (day/week) WR > individual trade WR due to diversification. Correlated trades: portfolio WR ≈ individual WR (no benefit). Calculate: portfolio daily WR depends on number of trades, individual WR, and correlation structure. Use simulation to estimate if analytical solution is complex.
Bonferroni: divide significance level by number of tests (α' = α/n). Conservative but simple. Benjamini-Hochberg: controls False Discovery Rate, less conservative. Use when testing many segments (strategies, instruments, time periods). Without correction, 5% of null segments will appear significant by chance.
Use regression-based attribution: regress win/loss outcomes on potential factors (entry signal, market condition, timing, etc.). Coefficients show marginal contribution. Alternatively, use decision trees to identify which factors most strongly predict wins. Compare WR in presence vs absence of each factor to quantify contribution.
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