If you’ve ever come across a trading strategy that claims “just reverse the rules for shorts”, you might want to rethink it. Many trading educators and strategy sellers promote setups where long and short trades follow identical logic—just flipped. They’ll show 100-day backtests with seemingly great results, often demonstrating the strategy’s success in a video. I used to believe in these strategies. I even went the extra mile—automating them, backtesting them across multiple instruments, timeframes, and sessions. Yet, no matter how much I optimized, none of them held up in live trading. I blamed everything: the time of day, the volatility, even the broker’s data. That was until I discovered something eye-opening using NinjaTrader’s Strategy Analyzer with its optimizer function. It completely changed the way I approach backtesting and strategy development. Let’s break it down. Why One-Size-Fits-All Strategies Fail The idea that you can just reverse a long trade to create a short trade sounds logical—but the market doesn’t work that way. 1. Market Bias Exists - Most markets have an upward drift, meaning price action behaves differently on long and short trades. - Example: In stocks and indices, bullish moves often develop over time, while bearish moves tend to be sharp and fast (panic selling). 2. Risk is Asymmetrical - When you go long, your maximum loss is the amount invested. But when shorting, losses can be theoretically unlimited. - Because of this, short strategies require different stop-loss logic, entry timing, and trade management than longs. 3. Behavioral & Liquidity Differences - Retail traders buy pullbacks, institutions hunt stop losses, and algorithms exploit weak hands—but they behave differently in long vs. short conditions. - This means momentum, order flow, and liquidity dynamics aren’t symmetrical. Still not convinced? Let’s look at what happens when you try to backtest these strategies without proper optimization. The Backtesting Illusion: Why Most Strategies Look Great in Hindsight Many traders rely on historical backtests to prove a strategy’s profitability. But without proper optimization, backtests can be misleading. 1. The Optimization Trap (a.k.a. Curve Fitting) - Many backtests look great because they’re optimized to historical data, not future price action. - If you tweak parameters until a strategy performs well on past data, you’re just fitting it to noise—not uncovering a real edge. - A moving average crossover strategy, for example, might look great when optimized over the past 100 days, but fails miserably on the next 100 days. 2. Long and Short Trades Often Require Different Parameters - While testing, I found that even something as simple as moving averages needed different settings for longs vs. shorts to be profitable. - A 10/50 EMA crossover might work well for longs, but fail for shorts—yet many strategies apply the same settings for both. - Using NinjaTrader’s Strategy Analyzer, I discovered that optimizing long and short conditions separately gave far better results. The Right Way to Backtest & Optimize Strategies If you want real trading strategies (not just curve-fitted models that work on past data), follow these steps: 1. Treat Long & Short Trades Separately - Test different parameters for long and short trades. - Optimize each independently instead of assuming a simple reversal works. 2. Use Out-of-Sample Testing - Never optimize and test on the same dataset. - Keep a portion of data unseen (out-of-sample) to validate performance. 3. Walk-Forward Optimization - Instead of running a single optimization, use walk-forward analysis—optimize on one dataset, test on another, then repeat in rolling windows. 4. Avoid Overfitting with Fewer Parameters - If a strategy needs 10+ conditions to be profitable, it’s curve-fitted. - Stick to core price action-based logic that makes sense across different market conditions. Watch this video on NinjaTrader’s optimization process to understand it better: Case Study: Backtesting ICT Unicorn At ScalperIntel, we take a modern approach to strategy development—ensuring traders can backtest and optimize strategies correctly. One of our most powerful tools, ICT Unicorn, follows institutional liquidity principles. When backtested properly using NinjaTrader’s Strategy Analyzer, we found: ✔ Optimizing long and short conditions separately improved results significantly. ✔ Using correct entry and exit conditions per trade direction increased win rates. ✔ When used in live market conditions, it maintained profitability across multiple datasets. Want to see it in action? Watch this video where we optimize ICT Unicorn Settings: Final Thoughts: The ScalperIntel Advantage Most trading strategies fail because they rely on simplistic, symmetrical logic that doesn’t reflect real market conditions. At ScalperIntel, we design modern indicators built for real trading—fully customizable, backtestable, and ready for automation in NinjaTrader. Want to start optimizing your strategies the right way? Explore our tools at ScalperIntel.com and take your trading to the next level. Start enhancing your trading strategies today! Explore our ScalperIntel indicators, tailored for NinjaTrader and fully supporting BloodHound automation. Visit ScalperIntel.com to learn more and elevate your trading.
The posted videos both use back testing in ninja which is worthless at least according to ninja. Too bad they don't have any actual trade results to show us. https://ninjatrader.com/support/hel...glish.html?discrepancies_real-time_vs_bac.htm
Agreed that there are some meaningful differences long vs short. I have 1 trade type I only use short, and I set slightly different targets long vs short as well for that same reason. As they say, stocks takes the escalator up and the elevator down.