Quote from jedwards:
Curve fitting is when you over-optimize the input parameters of a trading strategy to the point where you maximize profits, but the actual results won't produce a winning strategy in real life.
You can over-optimize any strategy to produce fantastic results. Take any simple MACD crossover on NT, and optimize the results based on various EMAs. You'll find that you will probably have a set of input parameters that will produce fantastic results.
That's because you are optimizing your results for past results, including the noise. This will produce great results for the past, however, going forward, because your strategy was optimized for the entire dataset, including noise, it likely won't be applicablein the future, and you'll likely have terrible real-life performance.
For a strategy to work, it needs to work on a genuine, reproducible behavioral trait of the markets. That means that it needs to not only work on historical data, but data that you've never seen before.
If you used NT backtesting and optimization to generate your strategy, then it likely is unsuccessful strategy. What you should do is take a subset of the data, optimize it for that subset, and then apply the strategy to the rest of the data, and see if the returns you see on the subset work across the entire dataset. If so, then you *might* have a good strategy. Then, you need to forward test it against new, real data to see if it still hold.