Handle Broker Diff?
Handling variations in price data, bid/ask spreads, slippage, and other broker-specific factors poses a common challenge in trading. While market prices may align across brokers, execution details can differ, impacting candlestick formation, bid/ask spreads, and more.
To enhance strategy robustness, shift from absolute values to relative metrics or ratios. For instance, express spread as a percentage of the current price to mitigate sensitivity to absolute prices.
Utilizing moving averages and technical indicators aids in smoothing data noise resulting from broker-specific manipulations. Emphasizing trends and relative changes rather than absolute values enhances strategy resilience.
Benchmarking your strategy across various brokers is valuable, especially if it heavily relies on one broker. Analyzing performance across platforms helps identify and adapt to broker-specific variations, fostering a more universal strategy.
Implementing robust risk management, such as setting stop-loss orders and employing position sizing strategies, is crucial. Being mindful of slippage and spread impacts on trades enhances overall risk mitigation.
While no strategy can entirely eliminate broker-specific variations, incorporating these considerations enhances adaptability and resilience across different brokers.
I believe a small edge like 55% in a strategy would not work. You should rely on strategies that offer at least 65% win rate.(considering RR=1:1)
A 65% win rate ends up 55% in reality.
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A strategy can work specifically for one broker but it cannot work for another broker, knowing that in the end the prices are always the same everywhere with the only difference that the brokers make manipulations to the formation of candles (OHLC) of prices as well as manipulating the bid/ask or spread, slippage, gaps, in your favor, how to handle this problem?
I was thinking about instead of using absolute prices, applying an MA or would it be better to standardize or normalize the OHLC input data? what its better?
note: Not for a learning model.