Quantitative trading strategies harnessing the power of quantitative techniques to create a winning trading program:
Optimization allows the trader to fine tune a strategy. however many believe fitting strategies to past data yields unrealistic expectations.
parameters are tweaked once a successful strategy is found. ex. Length of the moving average, the volatility multiplier.
to simplify the number of parameters we can use more than one unit per test. for example increment by five insread of one.
optimization is a technique to maximize the expected value of a trading strategy.
The first would use parameters for the current period that had performed best in the prior period. The idea behind this strategy is that strings of past performance are likely to continue, and that as traders we want to stay with parameter sets that are performing the best. The second test selected parameter sets for the current period that performed the worst in the prior period. The idea behind this strategy is that performance is likely to mean revert over time. Parameter sets that have been “cold” and performing poorly are likely to revert and perform well in the future.
avoid trend-following signals when futures are caught in trading ranges but to take trend-following signals when stocks are in trading ranges.
based on the ADX indicator we can select a specific strategy
ADX<15 RSI osscillator
ADX>25 Channel Breakout
Positive autocorrelation exists when greater than average values tend to lead to greater than average values in the next period, and vice versa. Negative autocorrelation exists when greater than average values lead to less than average values.
markets trend roughly 60 percent of the time. The fact that the average falls greater than 50 percent suggests that markets do in fact trend, and we can apply trend-following strategies to exploit this inefficiency.
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