A trader notices that markets sometimes continue moving after breaking above a recent high. Another sees that sharp declines are often followed by partial recoveries. Someone else observes that a market behaves differently when volatility is rising.

These observations may be useful.

But they are not yet strategies.

Consider the statement:

Markets often continue rising after a strong breakout.

This is a trading idea. It suggests that momentum may persist.

It does not tell us:

• What counts as a breakout

• What counts as strong

• When the position should be opened

• Which markets should be traded

• How much should be risked

• When the position should be closed

• What costs should be included

• How the idea should be evaluated

Until these choices are made explicitly, the idea cannot be tested consistently.

The purpose of strategy design is to transform a broad market observation into a complete set of rules.

Observation → hypothesis → rules → test → interpretation

This article explains that process.

Begin with the market behaviour

A strategy should start with an idea about market behaviour, not with a collection of indicators.

The starting point might be:

• Trends tend to persist

• Extreme short-term moves tend to reverse

• Quiet markets are often followed by large moves

• Strong markets tend to remain stronger than weak markets

• Prices react differently during certain sessions

• Volatility tends to cluster

• Markets behave differently above and below long-term trend levels

The idea does not have to be original.

Many successful trading strategies are based on behaviours that have been studied for decades. What matters is whether the idea can be expressed clearly and tested honestly.

A useful market idea should answer:

What behaviour am I trying to capture?

For example:

I want to test whether a market that closes above its recent trading range is more likely to continue upward than immediately reverse.

This is much more useful than:

I want to build a profitable breakout strategy.

The first statement describes a behaviour. The second describes a desired result.

Research should begin with the behaviour.

Turn the observation into a hypothesis

A hypothesis is a specific claim that can be tested.

Suppose the observation is:

Markets sometimes trend after reaching a new high.

A possible hypothesis is:

When a liquid market closes above the highest close of the previous 20 bars, its return over the following 10 bars is positive more often than normal.

This hypothesis identifies:

• The event: a close above the previous 20-bar high

• The market type: liquid instruments

• The observation period: the following 10 bars

• The expected result: stronger-than-normal positive returns

The hypothesis may turn out to be false.

That is acceptable.

The goal of research is not to protect the original idea. It is to determine whether the evidence supports it.

Separate the hypothesis from the strategy

A hypothesis asks whether a market behaviour exists.

A strategy defines how that behaviour might be traded.

These are related, but they are not identical.

For example, the hypothesis might be:

Returns following a 20-bar breakout are positively biased.

The strategy might then say:

Enter long after a 20-bar breakout, use an ATR-based stop, and exit after a 10-bar low.

The stop and exit do not test whether breakout momentum exists. They determine how the potential behaviour is converted into a trade.

This distinction matters because a poor strategy design can hide a valid market effect.

A breakout may have positive average forward returns, but a very tight stop could cause many trades to exit before the move develops. The researcher might incorrectly conclude that breakouts do not work.

The reverse can also happen. A complicated exit may create an attractive historical result even when the original market hypothesis is weak.

Test the behaviour first. Then design the trading rules.

Define the market universe

A strategy cannot be tested without deciding what it trades.

“Stocks,” “crypto,” and “futures” are too broad.

A market universe should specify the instruments that qualify.

Examples include:

• Current members of a major equity index

• The 100 most liquid US stocks

• Major currency pairs

• A fixed list of liquid futures contracts

• Bitcoin and Ethereum

• One specific index future

• All instruments meeting a minimum volume requirement

The universe matters because results can be distorted by market selection.

Suppose a trader tests a trend-following idea only on gold after noticing that gold had a strong historical trend. The result may reflect favourable instrument selection rather than a generally useful strategy.

A stronger test might include:

• Equity indices

• Bonds

• Metals

• Energy

• Agricultural commodities

• Currencies

A strategy does not have to work equally well on every market. But testing across several instruments helps reveal whether the idea is broad or dependent on one unusually favourable history.

Define the timeframe

Every rule depends on a timeframe.

A 20-bar breakout means something different on:

• A one-minute chart

• A 15-minute chart

• A four-hour chart

• A daily chart

• A weekly chart

The timeframe affects:

• Trade frequency

• Transaction costs

• Signal noise

• Holding period

• Execution difficulty

• Required market data

• Exposure to overnight gaps

The research plan should state the timeframe before testing begins.

For example:

Use daily bars and calculate all signals after the daily close.

That rule removes an important ambiguity.

It also avoids mixing bar-close information with intrabar execution assumptions.

Define the setup

A setup describes the market condition that must exist before an entry can occur.

Suppose the original idea concerns breakouts.

Possible setup rules include:

• Price is above its 200-day moving average

• Volatility is above its 50-day median

• The market has not triggered another breakout in the previous 10 bars

• Average volume is above a minimum threshold

• The current contract is not close to expiration

• The instrument has sufficient liquidity

A setup should have a reason.

Adding conditions only because they improve the historical result is dangerous. Each filter reduces the number of trades and creates another opportunity for overfitting.

A useful question is:

Why should this condition improve the logic of the strategy?

For example:

Require price to be above its 200-day moving average because long breakouts may be more reliable during established upward regimes.

This is a testable explanation.

By contrast:

Use a 173-day moving average because it produced the highest return.

That is probably data mining unless there is an independent reason for the choice.

Define the entry event

The entry rule must identify the exact event that creates a trade.

A vague rule might say:

Buy when price breaks resistance.

A testable version might say:

Enter long when the current daily close is greater than the highest daily close of the previous 20 completed bars.

This still leaves an execution question.

Should the strategy enter:

• At the current close

• At the next bar’s open

• With a stop order above the breakout level

• With a limit order after a pullback

Each choice creates a different strategy.

A conservative bar-close version might be:

Calculate the signal after the daily bar closes and enter at the next bar’s open.

That avoids assuming that the strategy knew the final closing price before the bar had finished.

Use completed bars correctly

Suppose the rule is:

Enter when the closing price exceeds the previous 20-bar high.

The current bar should normally not be included in the previous 20-bar calculation.

Otherwise, the current close is being compared with a range that already contains the current close.

The intended logic is usually:

Compare the current close with the highest close of the 20 bars completed before the current bar.

This small distinction can change the signals.

Precise indexing matters in both research and code.

Define long and short rules separately

A short strategy is not always the exact reverse of a long strategy.

Markets often rise and fall differently.

Equity indices, for example, may:

• Rise gradually

• Fall sharply

• Have a long-term upward bias

• Experience larger volatility during declines

A rule that works for long positions may not work equally well when reversed.

Instead of assuming symmetry, define and test both sides separately.

For example:

Long rule

Enter long after a close above the previous 20-bar high while price is above the 200-day moving average.

Short rule

Enter short after a close below the previous 20-bar low while price is below the 200-day moving average.

These rules look symmetrical, but their results should still be evaluated independently.

Define position sizing

The entry rule decides when to trade.

Position sizing decides how much the trade matters.

Common sizing methods include:

Fixed quantity

Trade one contract or 100 shares.

This is simple but does not account for changes in price or volatility.

Fixed capital

Allocate $10,000 to each trade.

This keeps the investment amount stable but not necessarily the risk.

Percentage of equity

Allocate 10% of current account equity to each position.

This adjusts as the account grows or declines.

Fixed risk

Risk 1% of account equity based on the distance to the initial stop.

This attempts to equalize the planned loss across trades.

Volatility-adjusted sizing

Reduce position size when volatility is high and increase it when volatility is low.

This can help normalize exposure across instruments.

Position sizing should be selected before examining the final performance chart.

Otherwise, leverage can be adjusted until an average strategy appears impressive.

Define the initial risk

A strategy should state what invalidates the trade or limits the loss.

Possible initial risk rules include:

• A fixed percentage stop

• A fixed price-distance stop

• An ATR-based stop

• A recent swing low or high

• A time-based exit

• No stop, but a portfolio-level risk limit

An ATR-based rule might be:

Place the initial stop two times the 14-bar ATR below the entry price.

This still requires further details:

• Is ATR measured on the signal bar or entry bar?

• Is the stop fixed after entry?

• Is it recalculated on every bar?

• What happens when the market gaps beyond the stop?

• Is the exit assumed at the stop price or next available price?

Rules that appear simple often contain hidden assumptions.

Define the exit rule

The exit should be specified as carefully as the entry.

Possible exit rules include:

• Exit below a recent low

• Exit after a moving-average crossover

• Exit after a fixed number of bars

• Exit at a profit target

• Exit with a trailing stop

• Exit when an opposite signal occurs

• Exit when the original setup is no longer valid

For example:

Exit long when the daily close falls below the lowest close of the previous 10 completed bars. Execute at the next bar’s open.

This is testable.

A rule such as “exit when momentum weakens” is not testable until momentum and weakening are both defined.

Distinguish signal price from execution price

A signal may be generated at one price and executed at another.

Suppose a breakout is confirmed at the daily close.

The signal price is the closing price.

But if the strategy waits until the bar is complete, the earliest simple execution assumption may be the next bar’s open.

That difference can matter when the market gaps.

A backtest that enters at the same close used to generate the signal may be using information that was not available early enough to trade at that price.

Every strategy should distinguish:

• When the information becomes available

• When the decision is made

• When the order is submitted

• Which price is assumed for execution

Define commissions and slippage before testing

Transaction costs should be included from the beginning.

Suppose a strategy makes a small average profit per trade. It may appear successful before costs and fail after realistic costs are added.

A simple cost model might state:

Include a commission of $2 per order and slippage of one minimum price increment on entry and exit.

For stocks, the assumptions might include:

• Per-share commission

• Minimum commission

• Bid-ask spread

• Percentage slippage

For futures:

• Broker commission

• Exchange and clearing fees

• Tick size

• Contract multiplier

• Rollover treatment

For crypto:

• Maker or taker fees

• Spread

• Funding costs

• Exchange-specific execution differences

The exact model will never be perfect. But zero-cost assumptions are rarely realistic.

Define how overlapping signals are handled

Suppose a long position is already open and another long signal appears.

What should the strategy do?

Possible rules include:

• Ignore the new signal

• Add another position

• Reset the stop

• Recalculate position size

• Replace the existing entry price

• Allow a maximum number of additions

This is the issue of pyramiding.

A strategy should also explain what happens when an opposite signal appears:

• Close the existing position only

• Close and reverse immediately

• Wait until the next bar

• Ignore the opposite signal until the current trade exits

Leaving these choices undefined can produce inconsistent results.

Define the testing period

The selected historical period can strongly influence performance.

A strategy tested only during a strong bull market may look more reliable than it really is.

The test should ideally include different environments:

• Bull markets

• Bear markets

• Sideways markets

• High-volatility periods

• Low-volatility periods

• Crisis periods

• Calm periods

• Rising-rate and falling-rate environments

The objective is not to find a period in which the strategy performs best.

It is to understand when the strategy performs well and when it struggles.

Choose evaluation metrics before viewing the result

The criteria used to judge the strategy should be selected in advance.

Otherwise, the researcher may focus on whichever statistic looks most favourable.

Useful metrics include:

• Total return

• Annualized return

• Maximum drawdown

• Number of trades

• Win rate

• Average trade

• Average win

• Average loss

• Payoff ratio

• Profit factor

• Sharpe ratio

• Exposure

• Time in market

• Longest drawdown duration

• Performance after costs

The importance of each metric depends on the strategy.

A high-frequency strategy requires careful attention to costs. A trend-following strategy may have a low win rate but a high payoff ratio. A long-term strategy may have few trades and therefore limited statistical evidence.

Example: turning an idea into rules

Consider the following initial idea:

Strong markets tend to continue rising.

This is not testable.

We can gradually convert it into a research plan.

Step 1: Define strong

A market is strong when it closes above the highest close of the previous 20 completed bars.

Step 2: Define the regime

Only consider long signals when price is above its 200-day simple moving average.

Step 3: Define the market

Test the strategy on liquid equity-index, bond, currency, metal, and energy futures.

Step 4: Define the timeframe

Use daily bars.

Step 5: Define the signal timing

Calculate the signal after the daily bar closes.

Step 6: Define execution

Enter at the next bar’s open.

Step 7: Define the initial stop

Set the initial stop two times the 14-day ATR below the entry price.

Step 8: Define position size

Size the position so that the distance from the entry to the initial stop represents 1% of current account equity.

Step 9: Define the exit

Exit when the daily close falls below the lowest close of the previous 10 completed bars. Execute at the next bar’s open.

Step 10: Define additional entries

Allow only one open position per market. Ignore new long signals while the position remains open.

Step 11: Define costs

Include estimated commissions and one tick of slippage on every entry and exit.

Step 12: Define evaluation criteria

Evaluate net return, maximum drawdown, number of trades, win rate, payoff ratio, profit factor, Sharpe ratio, and performance across different markets and periods.

The original statement has now become a strategy that can be implemented.

Write the complete rule set

The strategy can be summarized as follows.

Market universe

A diversified list of liquid futures markets.

Timeframe

Daily bars.

Direction

Long only.

Trend filter

The closing price must be above the 200-day simple moving average.

Entry signal

The current closing price must be above the highest closing price of the previous 20 completed bars.

Execution

Enter at the next bar’s open.

Position size

Risk 1% of current account equity based on the distance to the initial stop.

Initial stop

Two times the 14-day ATR below the entry price.

Exit signal

The current closing price falls below the lowest closing price of the previous 10 completed bars.

Exit execution

Exit at the next bar’s open, unless the protective stop is triggered earlier.

Pyramiding

Not allowed.

Costs

Include commissions and estimated slippage.

Maximum positions

Apply a portfolio-level limit to prevent excessive total exposure.

This does not mean the strategy is profitable.

It means the strategy is defined.

That is the necessary first step.

Avoid changing too many things at once

Once the first test is complete, the temptation is to modify everything:

• Change the breakout from 20 bars to 50

• Add an RSI filter

• Replace the exit

• Change the stop

• Add a volume rule

• Test another timeframe

• Increase leverage

If several components change at once, it becomes difficult to understand why the result changed.

A better process is to alter one assumption at a time.

For example:

1. Test the 20-bar breakout without a trend filter.

2. Add the 200-day trend filter.

3. Compare several nearby breakout periods.

4. Compare fixed sizing with volatility-adjusted sizing.

5. Test alternative exits.

6. Increase cost assumptions.

7. Test additional markets.

This approach reveals which components contribute meaningful value.

Test ranges, not perfect parameters

Suppose the strategy performs best with a 37-bar breakout.

That does not necessarily mean 37 bars has special significance.

The stronger question is:

Does the idea work reasonably well across a range of similar values?

For example:

• 20 bars

• 30 bars

• 40 bars

• 50 bars

• 60 bars

A stable strategy may produce acceptable results across a broad region.

An overfit strategy may perform extremely well at 37 bars and poorly at 36 or 38.

Prefer stable regions to isolated peaks.

Keep an untouched test period

One of the biggest research mistakes is using all available data to design the strategy.

If every rule and parameter was chosen after examining the entire history, the final backtest is not an independent test.

A basic solution is to divide the data.

In-sample period

Use this period to develop the rules.

Out-of-sample period

Keep this period untouched until the strategy design is reasonably complete.

The out-of-sample result provides a more honest test of whether the logic survives on data that did not directly influence the design.

It is still not a guarantee of future performance.

But it is more informative than repeatedly optimizing on the same history.

Record each research decision

A simple research log can prevent accidental hindsight.

For every test, record:

• The hypothesis

• The rule being changed

• The reason for the change

• The markets tested

• The date range

• The cost assumptions

• The result

• The conclusion

• The next test

For example:

Hypothesis: Long breakouts may perform better when the long-term trend is positive.
Change: Add a 200-day moving-average filter.
Reason: Avoid long entries during established downward regimes.
Result: Drawdown decreased, but trade count and total return also fell.
Conclusion: The filter may improve risk-adjusted performance but reduces opportunity.

This is more useful than saving only the best equity curve.

Common mistakes when defining rules

1. Using subjective language

Examples:

• Strong trend

• Important support

• Large candle

• Weak momentum

• Good volume

• Clear breakout

These descriptions need numerical definitions.

For example:

Define a large candle as a true range greater than 1.5 times the 20-bar median true range.

The definition may later be changed, but it is now testable.

2. Selecting rules after viewing the chart

It is easy to look at historical charts and create rules that fit visible turning points.

This is hindsight.

A testable process defines the rules first and applies them to all qualifying data, including unattractive examples.

3. Ignoring failed signals

A trader may remember the breakout that began a major trend and overlook the many breakouts that quickly reversed.

The backtest must include every signal that met the rule.

4. Using future information

Examples include:

• Entering at a close that was not known until the bar ended

• Using the completed day’s high before the day was finished

• Referencing future bars

• Using revised data that was unavailable historically

• Selecting stocks based on their current index membership

These errors can create results that could not have been achieved in real time.

5. Adding filters until the backtest looks good

Every additional condition can improve historical fit.

It can also make the strategy less likely to work on new data.

A filter should solve a clearly identified problem, not merely raise net profit.

6. Testing only one market

A strategy may appear successful because the selected market happened to suit it.

Testing several markets helps distinguish a general behaviour from a favourable example.

7. Ignoring execution

A signal is not a fill.

Strategies using limit orders, stop orders, intrabar conditions, or illiquid instruments require careful execution assumptions.

8. Optimizing position size instead of the idea

Increasing leverage can improve return statistics while hiding weak underlying performance.

Evaluate the unleveraged logic first.

A reusable strategy specification

Before coding a strategy, complete the following template.

Trading idea

What market behaviour might create an opportunity?

Hypothesis

What specific claim will be tested?

Market universe

Which instruments qualify?

Timeframe

Which bar interval will be used?

Session

Which trading hours and timezone apply?

Direction

Long, short, or both?

Setup

What conditions must exist before entry?

Entry signal

What exact event triggers the trade?

Signal timing

When does the required information become available?

Execution

Which order type and assumed fill price are used?

Position size

How is trade quantity calculated?

Initial risk

Where is the initial stop or invalidation point?

Exit

What exact event closes the trade?

Additional entries

Is pyramiding allowed?

Opposite signals

Does the strategy close, reverse, or ignore them?

Costs

Which commissions, spread, slippage, and financing costs are included?

Portfolio constraints

What limits total exposure?

Test period

Which dates are included?

Out-of-sample period

Which data remains untouched during development?

Evaluation

Which metrics will determine whether the idea deserves further research?

When every field has an answer, the strategy is ready to be coded.

The core principle

A trading idea becomes useful when it can fail clearly.

That may sound strange, but it is essential.

A statement such as:

Buy when the market looks strong.

can always be reinterpreted after the result is known.

A rule such as:

Enter long at the next bar’s open after a daily close above the previous 20-bar high.

can be tested and rejected.

That is a strength.

Clear rules allow the market to provide evidence.

The objective is not to make every idea survive. It is to eliminate ambiguity and discover which ideas deserve further investigation.

A strategy should be precise enough to code, simple enough to explain, and explicit enough to prove wrong.

That is how a trading idea becomes a testable system.

Next in Strategy

Expectancy: The Number That Matters More Than Win Rate

The next article will explain how win rate, average gain, and average loss combine to determine whether a strategy has a positive long-term expectation.

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Educational content only. Nothing in this publication is investment advice or a recommendation to trade any financial instrument.

Buy when two moving averages cross. Sell when price reaches a new low. Enter when the RSI becomes oversold. Trade when the market breaks out of a range.

These can all be useful ideas, but none of them is a complete trading strategy.

An entry signal answers only one question:

When might I open a position?

A trading strategy must answer several more:

• What will I trade?

• On which timeframe?

• How much will I buy or sell?

• When will I exit?

• How much am I willing to lose?

• What happens when several signals occur together?

• How will commissions, spread, and slippage affect the result?

• Under which conditions should the strategy remain inactive?

Until these questions have explicit answers, there is nothing that can be tested consistently.

There is only an idea.

A strategy is a decision-making system

A trading strategy is a set of rules that converts market information into trading decisions.

Those decisions normally include:

1. Whether to enter

2. Whether to remain in a position

3. Whether to reduce or increase exposure

4. When to exit

5. How much capital to risk

The rules do not have to be complicated.

A strategy may be based on one moving average, a price breakout, a volatility measurement, or the relationship between two markets. Complexity does not automatically make a system more effective.

What matters is that the rules are sufficiently clear that two people applying them to the same data would reach the same decision.

Consider this instruction:

Buy when the market looks strong.

It may sound reasonable, but it is not testable. Different traders will interpret “strong” differently.

Now consider this rule:

Enter long when today’s closing price is higher than the highest closing price of the previous 20 completed bars.

This rule is explicit.

It can be coded, tested, repeated, and challenged.

That is the beginning of a systematic strategy.

Strategy, setup, indicator, and system

These terms are often used as though they mean the same thing, but they describe different things.

Indicator

An indicator transforms market data into another value.

A moving average, Average True Range, RSI, and MACD are indicators. They describe some aspect of price, momentum, or volatility.

An indicator does not automatically tell you what action to take.

Setup

A setup describes a market condition that may be interesting.

Examples include:

• Price trading above a long-term moving average

• Volatility falling to a six-month low

• A market reaching a new 50-day high

• A fast moving average crossing above a slow moving average

A setup helps identify a possible opportunity, but it may not define the complete trade.

Entry signal

An entry signal defines the event that opens a position.

For example:

Enter long at the next bar’s open after the market closes above its previous 20-bar high.

This is more precise than a setup, but it still does not explain position size or exit logic.

Trading strategy

A strategy combines the entry rule with position sizing, risk management, exit rules, trading costs, and execution assumptions.

Trading system

The terms “strategy” and “system” are often used interchangeably.

A broader trading system may also include:

• A portfolio of several strategies

• Market-selection rules

• Capital allocation

• Operational controls

• Broker and execution procedures

• Monitoring and review processes

In AlgoSolo, a strategy means the complete rules required to test and execute one defined trading approach.

The components of a complete trading strategy

A well-defined strategy should contain at least the following components.

1. Market universe

The strategy must define which instruments it is allowed to trade.

Examples:

• Stocks in the S&P 500

• Major currency pairs

• Liquid cryptocurrency markets

• Equity-index futures

• Gold and crude-oil futures

• One specific instrument, such as Bitcoin or the S&P 500

A result obtained on one market should not automatically be assumed to work on another.

Different instruments have different:

• Trading hours

• Volatility patterns

• Liquidity

• Contract specifications

• Transaction costs

• Long-term behaviour

The chosen market universe is therefore part of the strategy.

2. Timeframe

The same rule may produce very different results on a five-minute chart and a daily chart.

A strategy should identify:

• The chart timeframe

• The decision frequency

• Whether signals are evaluated during the bar or after it closes

• The trading session used

• The timezone used for session-based rules

For example:

Evaluate the strategy once per day after the official market close.

This is substantially different from recalculating the strategy after every price change.

3. Setup and market filter

A market filter determines when the strategy is permitted to trade.

A long-only strategy might require price to be above its 200-day moving average. A mean-reversion strategy might trade only when volatility is below a certain level. A futures strategy might avoid entering shortly before the contract expires.

Filters may improve a strategy, but they also create additional parameters. Every extra condition should have a defensible purpose.

4. Entry rule

The entry rule should specify:

• Long, short, or both

• The exact signal condition

• When the condition is evaluated

• The type of order used

• The assumed execution price

Compare these two descriptions:

Buy after a breakout.

and:

After the daily bar closes, enter long at the next bar’s open when the closing price is above the highest closing price of the previous 20 completed bars.

The second rule can be tested without interpretation.

5. Position size

A strategy must determine how much to trade.

Possible methods include:

• A fixed number of shares or contracts

• A fixed amount of capital

• A percentage of account equity

• A fixed percentage of equity at risk

• Volatility-adjusted sizing

• Equal allocation across markets

Position sizing can change the character of a strategy even when the entry and exit rules remain unchanged.

A strategy that risks 0.5% of account equity per trade is not equivalent to the same strategy risking 5%.

The signals may be identical, but the drawdowns and probability of ruin are not.

6. Exit rule

A strategy is incomplete without an exit.

Common exit methods include:

• Stop-loss exits

• Profit targets

• Trailing stops

• Moving-average exits

• Opposite signals

• Time-based exits

• Volatility-based exits

• Closing the position after a fixed number of bars

The exit rule often has as much influence on performance as the entry rule.

A trend-following entry combined with a tight profit target may prevent the strategy from capturing long trends. A mean-reversion strategy without a protective exit may remain exposed while a temporary deviation turns into a lasting market move.

The entry and exit must be designed as parts of the same system.

7. Risk controls

Position sizing controls risk at the trade level, but a complete strategy may also need broader limits.

Examples include:

• Maximum number of simultaneous positions

• Maximum exposure to one market

• Maximum sector exposure

• Maximum daily loss

• Maximum portfolio leverage

• Rules for correlated positions

• Suspension after a specified drawdown

These controls are especially important when the same strategy trades several instruments.

Ten positions do not necessarily represent ten independent risks. They may all respond to the same underlying market event.

8. Trading costs

Backtests that ignore trading costs can create an unrealistic impression of profitability.

Relevant costs may include:

• Broker commissions

• Exchange fees

• Bid-ask spread

• Slippage

• Borrowing costs for short positions

• Financing or funding costs

• Market-data costs

• Contract rollover costs

The impact depends on the strategy.

A long-term system making ten trades per year may be relatively insensitive to small commissions. A short-term strategy making hundreds of trades may become unprofitable after realistic costs are included.

Costs should be treated as part of the strategy, not added as an afterthought.

9. Execution assumptions

A backtest must make assumptions about how orders are filled.

Suppose a daily strategy produces a signal using the closing price. It cannot normally assume that the trade was also executed at that same closing price unless an appropriate order was actually available before the close.

A more conservative rule may be:

Calculate the signal after the bar closes and execute at the next bar’s open.

Other execution questions include:

• Can a limit order remain unfilled?

• What happens when price gaps beyond a stop?

• Are several orders allowed on the same bar?

• Is the strategy allowed to reverse immediately?

• Are partial fills possible?

• Is sufficient volume available?

These details can materially change a result.

10. Evaluation criteria

A strategy should be judged using more than net profit.

Useful measurements include:

• Total return

• Annualized return

• Number of trades

• Win rate

• Average winning trade

• Average losing trade

• Payoff ratio

• Profit factor

• Maximum drawdown

• Recovery time

• Exposure

• Sharpe ratio

• Performance after costs

No single number tells the complete story.

A high win rate can conceal rare but very large losses. A high total return may have required unacceptable leverage. An attractive Sharpe ratio may be based on too few trades to provide meaningful evidence.

The objective is not to find one flattering statistic. It is to understand how the strategy produces returns and what risks it takes while doing so.

An example of a complete strategy

Consider a simple long-only breakout strategy.

This example is deliberately basic. It is not a recommendation to trade.

Market

A liquid instrument with reliable daily price data.

Timeframe

Daily bars.

Evaluation

Signals are calculated after each daily bar has closed.

Market filter

The closing price must be above its 200-day simple moving average.

Entry

Enter long at the next bar’s open when the current closing price is higher than the highest closing price of the previous 20 completed bars.

Position size

Risk no more than 1% of current account equity based on the distance between the entry price and initial stop.

Initial stop

Place the initial stop two Average True Ranges below the entry price.

Exit

Exit at the next bar’s open when the closing price falls below the lowest closing price of the previous 10 completed bars.

Costs

Include commission and an estimated amount of slippage for every entry and exit.

Maximum exposure

Only one position may be open in the instrument at a time.

This description is still not perfect. For example, it should specify the ATR period, how position size is rounded, and what happens when price gaps beyond the stop.

But it is already much closer to a strategy than the statement:

Buy 20-day breakouts.

Why explicit rules matter

Clear rules make a strategy easier to improve.

When the result is disappointing, you can identify the component responsible:

• Does the entry occur too late?

• Does the exit cut profitable trades too quickly?

• Is the stop too close for the market’s normal volatility?

• Are costs consuming the expected return?

• Is the strategy active during unsuitable market regimes?

• Is position sizing creating excessive drawdowns?

Without explicit rules, changes tend to become emotional and inconsistent.

One losing trade leads to a wider stop. A missed trend leads to an earlier entry. A drawdown leads to adding another indicator. Soon the original strategy has been replaced by a collection of reactions.

Systematic research requires changing one defined assumption at a time and measuring the effect.

A strategy is not validated by one profitable backtest

Turning an idea into rules makes it testable. It does not prove that the strategy works.

A profitable historical result may be caused by:

• Random chance

• Overfitting

• Selection of a favourable market

• Selection of a favourable period

• Unrealistic execution

• Missing trading costs

• Lookahead bias

• Survivorship bias

• A market regime that may not return

The purpose of a backtest is not to manufacture the best possible result.

It is to challenge the strategy.

A useful research process asks:

Does the idea remain reasonable when the assumptions become less favourable?

That may involve testing:

• Different markets

• Different periods

• Nearby parameter values

• Higher trading costs

• Delayed execution

• Out-of-sample data

• Different volatility regimes

A robust strategy does not need to produce identical results everywhere. But its performance should not depend entirely on one perfect combination of settings.

Before testing a strategy, write it down

A practical habit is to write the complete strategy before running the backtest.

Use this checklist:

Market

What instruments may be traded?

Timeframe

When are decisions made?

Setup

What conditions must exist before an entry is permitted?

Entry

What exact event opens the position?

Position size

How much capital is allocated or placed at risk?

Exit

What exact event closes the position?

Costs

What commission, spread, and slippage assumptions are included?

Risk limits

What prevents one trade or group of trades from becoming excessively large?

Evaluation

Which measurements will determine whether the test is promising?

Writing these rules first reduces the temptation to repeatedly change the strategy until the historical result looks attractive.

The core principle

A trading strategy is not defined by how sophisticated it sounds.

It is defined by how clearly it makes decisions.

A useful strategy should allow you to explain:

What am I trading, why am I entering, how much am I risking, when will I exit, and under what conditions might the idea fail?

When those questions have precise answers, the strategy can be coded and tested.

That is where systematic trading begins.

Next in Strategy

From Trading Idea to Testable Rules

The next article will show how to take a general observation about market behaviour and convert it into rules that can be researched without ambiguity.

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Educational content only. Nothing in this publication is investment advice or a recommendation to trade any financial instrument.