Some Techniques to Reduce and Control Drawdown

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Some Techniques to Reduce and Control Drawdown
That’s why you never hear about eliminating them. Rather, it’s always about how to reduce a drawdown in Forex.To get more news about how to control drawdown, you can visit wikifx.com official website.
Here is a key point to take away: In Forex, the maximum drawdown should never exceed the distance between the entry and the stop loss, less any slippage and commissions.Let's say you are trading the USD/JPY Forex currency pair by going bullish with a long position. The entrance is $103.04. Set your target at recent highs at $103.32 and a stop at $102.94.

The distance between $103.04 and $102.94 is $0.10, or 0.097% loss. Now, let's say you are using 500x leverage and investing $1000 in a trade. The total potential loss would be $1,000 x 0.00097 x 500 = ~$485.
How to get out of the drawdown
As much as we do to prepare to reduce drawdowns in Forex, they will happen. The second half of the battle is managing them once they begin. The simplest and easiest way to stop a drawdown is to close out your positions.
Let’s say the trade setup is still valid, but you don’t like the total potential risk. You can always exit part of the position to reduce your total drawdown.Another option is to add a position that works in the opposite direction, like a hedge. For example, if you're long the US Dollar versus the Japanese Yen, you can hedge that by going long the Swiss Franc versus the US Dollar.
Whichever method you choose to reduce your drawdown depends on your skill and comfort level in trading Forex.
Psychology behind drawdowns
For most of us, our trades will go negative at some point. It’s rare to pick off the exact top or bottom.
When you create a rigid trading structure with clear entries, exits, stops, and risk management, drawdowns become part of your everyday life. You stop fighting them and start managing them.In fact, most of us only think of drawdowns for one of two reasons: we risked too much on the trade, or we didn’t define our stop loss. You can reduce your drawdown at any time using one of the strategies listed.
Drawdowns require a sort of meditative acceptance where you acknowledge them as a part of your trading. Only then can you begin to manage them.
Containment of drawdowns and optimization of performance ratios for multi-asset portfolios is critical for trading strategies. Alas, short data series or structural changes often render estimates of covariance matrices unreliable. A popular solution is risk-parity with volatility targeting. An alternative is ‘MinMax’ drawdown control, which builds on a broad interpretation of drawdowns as maximum actual or opportunity losses from not adjusting a benchmark portfolio to a specific underlying asset. In the case of one risky and one safe asset, this boils down to managing simultaneously the risks of conventional PnL drawdowns and foregone risk returns. Optimal asset allocation depends only on aversion to different types of drawdowns. Averaging over a plausible range of aversion parameters gives a model portfolio. Empirical evidence for the case of cryptocurrencies suggests that in an environment of uncertain returns MinMax delivers better PnL return-to-drawdown ratios than conventional volatility control.
A central challenge for portfolio allocation…[is] the implementation of mean-variance optimal portfolios…Using historical data to estimate returns and correlations, frequently lead to unattractive, leveraged, and highly unstable portfolios…The limitations of historical data as a determinant of portfolio allocation seem particularly salient in the case of innovative asset classes with limited track record… it is difficult to form reliable estimates of expected returns and covariance matrices needed as inputs for standard portfolio optimization. Even if such estimates are available, they may be useless to investors if the behavior of underlying assets changes over time.”
N.B.: This issue would apply to almost all financial contracts in the case of a significant structural change of the macro environment.
“Under the standard Bayesian framework, the investor is equipped with a prior…This prior captures the investor’s beliefs over the possible evolution of returns. This may include the presence of positive auto-correlation in returns, the possibility that a bubble may be underway, and so on…The difficulty we confront…is that forming beliefs is difficult, especially when there is little data to inform the decision-maker. An indicator of this difficulty is that different well-informed investors will frequently disagree about expected market behavior.”