Euro / U.S. DollarEducation
Trading Strategy? Strategies?
In finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets.
The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity.
For every trading strategy one needs to define assets to trade, entry/exit points and money management rules. Bad money management can make a potentially profitable strategy unprofitable.
The term trading strategy can in brief be used by any fixed plan of trading a financial instrument, but the general use of the term is within computer assisted trading, where a trading strategy is implemented as computer program for automated trading.
The trading strategy is developed by the following methods:
Trading Plan Creation; by creating a detailed and defined set of rules that guide the trader into and through the trading process with entry and exit techniques clearly outlined and risk/reward parameters established from the outset.
Automated trading; by programming or by visual development.
A trading strategy can be executed by a trader (Discretionary Trading) or automated (Automated Trading). Discretionary Trading requires a great deal of skill and discipline. It is tempting for the trader to deviate from the strategy, which usually reduces its performance.
An automated trading strategy wraps trading formulas into automated order and execution systems. Advanced computer modeling techniques, combined with electronic access to world market data and information, enable traders using a trading strategy to have a unique market vantage point.
A trading strategy can automate all or part of your investment portfolio. Trading models can be adjusted for either conservative or aggressive trading styles.
///
According to the No Free Lunch Theorem that states that any two optimization algorithms perform equally well when their performance is averaged across all possible problems.
Because of the close relationship between optimization, search, and machine learning, it also implies that there is no single best machine learning algorithm for predictive modeling problems such as Classification and Regression.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives.
In the simplest case, an optimization problem consists of maximizing or minimizing a real function by choosing input values from within an allowed set.
Adding more than one objective to an optimization problem adds complexity. When two objectives conflict, a trade-off is created.
There may be one lightest design, one stiffest design, and an infinite number of designs that fall somewhere in-between.
A design is judged to be optimal or efficient if it is not dominated by any other design.
The different solutions to determine the "favourite solution" is delegated to the decision maker.
In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative or necessarily related to each other*
///
Bias-Variance trade-off
The bias-variance trade-off is a central problem. Ideally one wants to choose a model that both accurately captures the regularities in the data, but also generalizes well to unseen data.
Unfortunately, it is typically impossible to do both simultaneously.
Overall, while the idea that the signal can logically be a function of relevant factors, the market being highly complicated, always changing, and not entirely efficient, a function derived from past data cannot reliably be used to predict future data for an extended period of time.
Due to the ever-changing dynamics of markets, what works one week/month is not guaranteed to work the next week/month. The significance of features is expected to change regularly and the common indicators often used by the majority of traders result in such predictions not giving much of an edge.
///
Algorithms are typically programmed with clear rules and are best at pursuing a single mathematical objective whilst humans often want multiple incompatible things.
When you start to deal with multiple, often competing, objectives a satisfactory mathematical solution doesn't always exist.
I believe that a trading edge exists when combining rule based algorithms and the ability of human decision-making to be some-what dynamic and adaptable to markets as a strict rules based hands off automated trading system is going to have ups and downs, the same way a discretionary trader will, however having the ability to turn on/off a trading system for example when fundamental mid/high impact data/news are released can gain/save you that extra couple of % in losses/profits.
Being dynamic in the markets and having the ability to switch on/off/between trading strategies at any given point can be an edge in itself imho.
I believe it boils down to understanding market environments and understanding that 'strategy A' will perform better than 'strategy B' under certain environments, can help you define R:R ratios, whether your looking at a potential intraday setup or a swing trade and how your going to manage your trade after it has moved in your desired direction.
EURUSD
👍
The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity.
For every trading strategy one needs to define assets to trade, entry/exit points and money management rules. Bad money management can make a potentially profitable strategy unprofitable.
The term trading strategy can in brief be used by any fixed plan of trading a financial instrument, but the general use of the term is within computer assisted trading, where a trading strategy is implemented as computer program for automated trading.
The trading strategy is developed by the following methods:
Trading Plan Creation; by creating a detailed and defined set of rules that guide the trader into and through the trading process with entry and exit techniques clearly outlined and risk/reward parameters established from the outset.
Automated trading; by programming or by visual development.
A trading strategy can be executed by a trader (Discretionary Trading) or automated (Automated Trading). Discretionary Trading requires a great deal of skill and discipline. It is tempting for the trader to deviate from the strategy, which usually reduces its performance.
An automated trading strategy wraps trading formulas into automated order and execution systems. Advanced computer modeling techniques, combined with electronic access to world market data and information, enable traders using a trading strategy to have a unique market vantage point.
A trading strategy can automate all or part of your investment portfolio. Trading models can be adjusted for either conservative or aggressive trading styles.
///
According to the No Free Lunch Theorem that states that any two optimization algorithms perform equally well when their performance is averaged across all possible problems.
Because of the close relationship between optimization, search, and machine learning, it also implies that there is no single best machine learning algorithm for predictive modeling problems such as Classification and Regression.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives.
In the simplest case, an optimization problem consists of maximizing or minimizing a real function by choosing input values from within an allowed set.
Adding more than one objective to an optimization problem adds complexity. When two objectives conflict, a trade-off is created.
There may be one lightest design, one stiffest design, and an infinite number of designs that fall somewhere in-between.
A design is judged to be optimal or efficient if it is not dominated by any other design.
The different solutions to determine the "favourite solution" is delegated to the decision maker.
In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative or necessarily related to each other*
///
Bias-Variance trade-off
The bias-variance trade-off is a central problem. Ideally one wants to choose a model that both accurately captures the regularities in the data, but also generalizes well to unseen data.
Unfortunately, it is typically impossible to do both simultaneously.
Overall, while the idea that the signal can logically be a function of relevant factors, the market being highly complicated, always changing, and not entirely efficient, a function derived from past data cannot reliably be used to predict future data for an extended period of time.
Due to the ever-changing dynamics of markets, what works one week/month is not guaranteed to work the next week/month. The significance of features is expected to change regularly and the common indicators often used by the majority of traders result in such predictions not giving much of an edge.
///
Algorithms are typically programmed with clear rules and are best at pursuing a single mathematical objective whilst humans often want multiple incompatible things.
When you start to deal with multiple, often competing, objectives a satisfactory mathematical solution doesn't always exist.
I believe that a trading edge exists when combining rule based algorithms and the ability of human decision-making to be some-what dynamic and adaptable to markets as a strict rules based hands off automated trading system is going to have ups and downs, the same way a discretionary trader will, however having the ability to turn on/off a trading system for example when fundamental mid/high impact data/news are released can gain/save you that extra couple of % in losses/profits.
Being dynamic in the markets and having the ability to switch on/off/between trading strategies at any given point can be an edge in itself imho.
I believe it boils down to understanding market environments and understanding that 'strategy A' will perform better than 'strategy B' under certain environments, can help you define R:R ratios, whether your looking at a potential intraday setup or a swing trade and how your going to manage your trade after it has moved in your desired direction.
EURUSD👍
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.