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The trader would then backtest the strategy, using actual data and would evaluate the strategy. The simulator would generate estimated number of trades, the fraction of winning/losing trades, average profit/loss, average holding time, maximum drawdown, and the overall profit/loss. The trader can then experiment and refine the strategy.
The ADX combines them and smooths the result with a smoothed moving average. To calculate +DI and -DI, one needs price data consisting of high, low, and closing prices each period (typically each day). One first calculates the directional movement (+DM and -DM): UpMove = today's high − yesterday's high DownMove = yesterday's low − today's low
Technical trading strategies were found to be effective in the Chinese marketplace by a 2007 study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction ...
Exponential moving averages highlight recent changes in a stock's price. By comparing EMAs of different lengths, the MACD series gauges changes in the trend of a stock. The difference between the MACD series and its average is claimed to reveal subtle shifts in the strength and direction of a stock's trend.
The Moving Median is a more robust alternative to the Moving Average when it comes to estimating the underlying trend in a time series. While the Moving Average is optimal for recovering the trend if the fluctuations around the trend are normally distributed, it is susceptible to the impact of rare events such as rapid shocks or anomalies.
Some commercial packages, like AIQ, use a standard exponential moving average (EMA) as the average instead of Wilder's SMMA. The smoothed moving averages should be appropriately initialized with a simple moving average using the first n values in the price series. The ratio of these averages is the relative strength or relative strength factor: