Are we still on track or is this the end? If I was trading I would be buying at this level - looks like we have back tested the .77 and holding nicely. The damage (as some expected) is done.
Backtesting is the general method for seeing how well a strategy or model would have done ex-post. Backtesting assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward.
Key Takeaways
Backtesting assesses the viability of a trading strategy or pricing model by discovering how it would have played out retrospectively using historical data.
The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future.
When testing an idea on historical data, it is beneficial to reserve a time period of historical data for testing purposes. If it is successful, testing it on alternate time periods or out-of-sample data can help confirm its potential viability.
Understanding Backtesting
Backtesting allows a trader to simulate a trading strategy using historical data to generate results and analyze risk and profitability before risking any actual capital.
A well-conducted backtest that yields positive results assures traders that the strategy is fundamentally sound and is likely to yield profits when implemented in reality. In contrast, a well-conducted backtest that yields suboptimal results will prompt traders to alter or reject the strategy.
Particularly complicated trading strategies, such as strategies implemented by automated trading systems, rely heavily on backtesting to prove their worth, as they are too arcane to evaluate otherwise.
As long as a trading idea can be quantified, it can be backtested. Some traders and investors may seek the expertise of a qualified programmer to develop the idea into a testable form. Typically, this involves a programmer coding the idea into the proprietary language hosted by the trading platform.
The programmer can incorporate user-defined input variables that allow the trader to "tweak" the system. An example of this would be in the simple moving average (SMA) crossover system. The trader would be able to input (or change) the lengths of the two moving averages used in the system. The trader could then backtest to determine which lengths of moving averages would have performed the best on the historical data.
The Ideal Backtesting Scenario
The ideal backtest chooses sample data from a relevant time period of a duration that reflects a variety of market conditions. In this way, one can better judge whether the results of the backtest represent a fluke or sound trading.
The historical data set must include a truly representative sample of stocks, including those of companies that eventually went bankrupt or were sold or liquidated. The alternative, including only data from historical stocks that are still around today, will produce artificially high returns in backtesting.
A backtest should consider all trading costs, however insignificant, as these can add up over the course of the backtesting period and drastically affect the appearance of a strategy’s profitability. Traders should ensure that their backtesting software accounts for these costs.
Out-of-sample testing and forward performance testing provide further confirmation regarding a system's effectiveness and can show a system's true colors before real cash is on the line. A strong correlation between backtesting, out-of-sample, and forward performance testing results is vital for determining the viability of a trading system.
HODL Tight XRP ARMY
Backtesting is the general method for seeing how well a strategy or model would have done ex-post. Backtesting assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward.
Key Takeaways
Backtesting assesses the viability of a trading strategy or pricing model by discovering how it would have played out retrospectively using historical data.
The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future.
When testing an idea on historical data, it is beneficial to reserve a time period of historical data for testing purposes. If it is successful, testing it on alternate time periods or out-of-sample data can help confirm its potential viability.
Understanding Backtesting
Backtesting allows a trader to simulate a trading strategy using historical data to generate results and analyze risk and profitability before risking any actual capital.
A well-conducted backtest that yields positive results assures traders that the strategy is fundamentally sound and is likely to yield profits when implemented in reality. In contrast, a well-conducted backtest that yields suboptimal results will prompt traders to alter or reject the strategy.
Particularly complicated trading strategies, such as strategies implemented by automated trading systems, rely heavily on backtesting to prove their worth, as they are too arcane to evaluate otherwise.
As long as a trading idea can be quantified, it can be backtested. Some traders and investors may seek the expertise of a qualified programmer to develop the idea into a testable form. Typically, this involves a programmer coding the idea into the proprietary language hosted by the trading platform.
The programmer can incorporate user-defined input variables that allow the trader to "tweak" the system. An example of this would be in the simple moving average (SMA) crossover system. The trader would be able to input (or change) the lengths of the two moving averages used in the system. The trader could then backtest to determine which lengths of moving averages would have performed the best on the historical data.
The Ideal Backtesting Scenario
The ideal backtest chooses sample data from a relevant time period of a duration that reflects a variety of market conditions. In this way, one can better judge whether the results of the backtest represent a fluke or sound trading.
The historical data set must include a truly representative sample of stocks, including those of companies that eventually went bankrupt or were sold or liquidated. The alternative, including only data from historical stocks that are still around today, will produce artificially high returns in backtesting.
A backtest should consider all trading costs, however insignificant, as these can add up over the course of the backtesting period and drastically affect the appearance of a strategy’s profitability. Traders should ensure that their backtesting software accounts for these costs.
Out-of-sample testing and forward performance testing provide further confirmation regarding a system's effectiveness and can show a system's true colors before real cash is on the line. A strong correlation between backtesting, out-of-sample, and forward performance testing results is vital for determining the viability of a trading system.
HODL Tight XRP ARMY