Risk-reward ratio in trading compares how much a trader plans to risk on a trade with how much potential reward the setup may offer.
For example, if a trader is prepared to risk ₹500 to pursue a potential ₹1,000 gain, the planned risk-to-reward ratio is:
₹500 : ₹1,000 = 1:2
In simple terms:
Risk 1 unit → Potential reward 2 units
But a favourable risk-reward ratio does not guarantee that a trading strategy will be profitable.
A strategy’s results depend on the complete relationship between:
Win Rate + Average Win + Average Loss + Position Size + Trading Costs + Slippage + Execution
This is why risk-reward ratio should be used as one part of a broader risk-management and strategy-evaluation process rather than as a fixed rule such as:
“Every trade must have a 1:2 ratio.”
This guide explains how to calculate risk-reward ratio, what 1R means, how breakeven win rates work, how expectancy connects risk with win rate, why planned and realised ratios can differ, and how position sizing affects account-level risk.
For a broader framework covering capital exposure, position sizing and trading risk, read How to Manage Risk in the Indian Stock Market.
Educational Disclaimer: This article is for general educational and informational purposes only. It does not constitute investment, financial, research or trading advice. Trading involves risk, including possible loss of capital. Stop-losses, targets, technical analysis, risk-reward ratios and positive historical expectancy cannot guarantee future trading results.
Quick Answer: What Is Risk-Reward Ratio?
The risk-reward ratio compares the amount a trader may lose if a trade reaches its predefined invalidation or stop level with the potential gain if the planned target is reached.
A simple formula is:
Risk-Reward Ratio = Potential Loss ÷ Potential Reward
Suppose:
Entry price = ₹1,000
Planned stop/invalidation = ₹975
Potential target = ₹1,050
The potential risk is:
₹1,000 − ₹975 = ₹25 per share
The potential reward is:
₹1,050 − ₹1,000 = ₹50 per share
Therefore:
₹25 : ₹50 = 1:2
The trader is risking ₹1 for every ₹2 of potential reward.
However, this ratio describes the planned trade structure.
It does not tell you:
- Whether the target will be reached
- How often the setup wins
- Whether the stop will execute exactly where expected
- Whether the trader exits early
- What trading costs will be
- Whether the strategy has positive expectancy
That requires additional analysis.
Risk-Reward Ratio vs Reward-Risk Ratio
A terminology issue frequently causes confusion.
This guide uses:
Risk : Reward
So:
1:2
means:
Risk 1 → Potential reward 2
Some trading platforms, books or calculators display the relationship in the opposite direction:
Reward : Risk
Under that convention, the same trade may be displayed as:
2:1
Neither mathematical representation is necessarily wrong.
The important thing is to know which convention is being used.
Throughout this article:
1:2 = risk one unit to potentially make two units
Risk-Reward Ratio Formula
For a long trade:
Risk per Share = Entry Price − Stop/Invalidation Price
Potential Reward per Share = Target Price − Entry Price
Then:
Risk-Reward Ratio = Risk ÷ Potential Reward
Example
Suppose:
- Entry = ₹800
- Stop = ₹780
- Target = ₹850
Risk:
₹800 − ₹780 = ₹20
Reward:
₹850 − ₹800 = ₹50
Therefore:
Risk : Reward = ₹20 : ₹50
Divide both sides by ₹20:
1 : 2.5
The planned risk-reward ratio is therefore:
1:2.5
Risk-Reward Ratio Example for a Short Trade
The calculation works in the opposite direction for a short position.
Suppose:
- Short entry = ₹500
- Invalidation = ₹515
- Target = ₹470
Potential risk:
₹515 − ₹500 = ₹15
Potential reward:
₹500 − ₹470 = ₹30
Therefore:
₹15 : ₹30 = 1:2
Again, this is only the planned ratio.
A short position also involves product-specific risks, execution conditions and potentially significant losses depending on the instrument and market structure.
What Does a 1:2 Risk-Reward Ratio Mean?
A 1:2 risk-reward ratio means the planned reward is twice the planned risk.
If planned risk is:
₹500
then a 1:2 setup would have potential reward of:
₹1,000
If planned risk is:
₹2,000
the corresponding potential reward would be:
₹4,000
The rupee amount changes.
The relationship does not.
This is one reason traders often express performance in R-multiples.
What Does 1R Mean in Trading?
1R represents one predefined unit of risk.
Suppose your planned maximum loss on a particular trade is:
₹800
Then:
1R = ₹800
A loss equal to the full planned risk is:
−1R = −₹800
A profit twice the initial risk is:
+2R = +₹1,600
A profit equal to half the initial risk is:
+0.5R = +₹400
A larger-than-planned loss of ₹1,200 would be:
−1.5R
R-multiples can make it easier to compare trades involving different securities, prices and position sizes.
For example:
| Trade | Rupee Result | R-Multiple |
|---|---|---|
| Trade A | -₹800 | -1R |
| Trade B | +₹1,600 | +2R |
| Trade C | +₹400 | +0.5R |
| Trade D | -₹400 | -0.5R |
The R-multiple shows the result relative to the amount originally defined as risk.
What Is the Breakeven Win Rate?
Risk-reward ratio becomes much more useful when combined with win rate.
The theoretical breakeven win rate tells you approximately how many trades need to win for average wins and losses to offset each other before costs.
If:
Average loss = 1R
and:
Average win = 2R
then the theoretical breakeven win rate is:
1 ÷ (1 + 2) = 33.33%
This means a strategy with an actual average winner of +2R and average loser of −1R would theoretically need to win more than one-third of its trades to have positive gross expectancy.
But this assumes the realised numbers truly match those averages.
Breakeven Win Rate by Risk-Reward Ratio
Here is the simplified relationship:
| Average Risk : Reward | Theoretical Breakeven Win Rate* |
|---|---|
| 1:0.5 | 66.67% |
| 1:1 | 50.00% |
| 1:1.5 | 40.00% |
| 1:2 | 33.33% |
| 1:2.5 | 28.57% |
| 1:3 | 25.00% |
| 1:4 | 20.00% |
*Before brokerage, taxes, slippage and other trading costs.
The formula is:
Breakeven Win Rate = Risk ÷ (Risk + Reward)
When risk is expressed as 1:
Breakeven Win Rate = 1 ÷ (1 + Reward Multiple)
This table is mathematically useful, but it should not be interpreted as saying a 1:4 strategy is automatically superior to a 1:2 strategy.
A larger target may be reached less frequently.
That changes the actual win rate.
Why a 1:2 Risk-Reward Ratio Does Not Guarantee Profitability
This is one of the most important concepts in risk-reward trading.
Imagine two strategies.
Strategy A
Average winner:
+2R
Average loser:
−1R
Win rate:
20%
Across 10 representative trades:
2 wins × +2R = +4R
8 losses × −1R = −8R
Gross result:
−4R
The strategy loses despite having a 1:2 average reward relative to risk.
Strategy B
Average winner:
+1.5R
Average loser:
−1R
Win rate:
50%
Across 10 representative trades:
5 wins × +1.5R = +7.5R
5 losses × −1R = −5R
Gross result:
+2.5R
before costs.
Therefore:
Risk-Reward Ratio Alone ≠ Trading Edge
The relationship that matters is:
Win Rate × Average Win
versus:
Loss Rate × Average Loss
Risk-Reward Ratio and Win Rate Work Together
A trading system can theoretically have:
Lower win rate + larger average winner
or:
Higher win rate + smaller average winner
and still produce positive expectancy.
Neither structure is automatically better.
For example:
System A
Win rate = 35%
Average win = +2.5R
Average loss = −1R
System B
Win rate = 60%
Average win = +1R
Average loss = −1R
Both need to be evaluated mathematically.
The correct question is not:
“What is the highest possible risk-reward ratio?”
It is:
“What combination of win rate, average win and average loss does my strategy actually produce over a sufficiently representative sample?”
Planned Risk-Reward vs Realised Risk-Reward
A trader may plan a 1:2 trade and still fail to achieve an average realised reward of 2R.
This distinction is extremely important.
Suppose a trader plans:
Stop = −1R
Target = +2R
But after reviewing 100 trades, the journal shows:
Average winner = +1.25R
Average loser = −1.10R
Why might this happen?
Possible reasons include:
- Taking profits early
- Moving targets
- Holding losses beyond the planned stop
- Slippage
- Gaps
- Partial exits
- Trailing stops
- Poor execution
- Brokerage and transaction costs
The chart may show a theoretical 1:2 setup.
But actual strategy performance is determined by:
Realised Average Win : Realised Average Loss
not the intended ratio written before entry.
Example: Planned 1:3 but Realised 1:1.4
Suppose a trader repeatedly plans:
Risk = 1R
Target = 3R
That looks attractive.
But the trader becomes nervous when trades move into profit and usually exits around +1.4R.
At the same time, losing trades average −1R.
The true observed relationship is therefore closer to:
1 : 1.4
rather than:
1 : 3
This is why maintaining a trading journal can be useful.
It allows the trader to compare:
Planned R:R
with:
Realised R:R
What Is Trading Expectancy?
Expectancy combines win rate with average wins and losses.
A simplified formula is:
Expectancy = (Win Rate × Average Win) − (Loss Rate × Average Loss)
Suppose historical strategy data shows:
- Win rate = 40%
- Loss rate = 60%
- Average winner = ₹2,500
- Average loser = ₹1,000
Then:
Winning component = 0.40 × ₹2,500 = ₹1,000
Losing component = 0.60 × ₹1,000 = ₹600
Gross expectancy:
₹1,000 − ₹600 = +₹400 per trade
Under those assumptions, the historical average gross expectancy would be:
+₹400 per trade
This does not mean the next trade is expected to produce exactly ₹400.
One trade may lose ₹1,000.
Another may gain ₹3,000.
Expectancy is an average concept calculated across a sample.
Future results can also differ from historical results.
Expectancy Using R-Multiples
Expectancy can also be expressed in R.
Suppose:
- Win rate = 45%
- Average winner = +2R
- Loss rate = 55%
- Average loser = −1R
Then:
(0.45 × 2R) − (0.55 × 1R)
=
0.90R − 0.55R
=
+0.35R
The simplified gross expectancy is:
+0.35R per trade
before costs.
If 1R equals ₹1,000, that corresponds to:
₹350 gross expectancy per trade
under those historical assumptions.
Expectancy After Trading Costs
Trading costs matter.
Suppose gross historical expectancy is:
+₹400 per trade
but average total transaction and execution costs are:
₹150 per trade
A simplified net calculation becomes:
₹400 − ₹150 = ₹250
So:
Gross Expectancy ≠ Net Expectancy
A strategy with only a small theoretical advantage can become unprofitable once:
- Brokerage
- Taxes and statutory charges
- Bid-ask spread
- Slippage
- Other execution costs
are incorporated.
This is especially relevant for high-frequency trading styles where transaction count is large.
A High Win Rate Can Still Lose Money
Consider ten hypothetical trades.
Nine trades make:
+₹500 each
Total gains:
9 × ₹500 = ₹4,500
One trade loses:
−₹6,000
Final result:
₹4,500 − ₹6,000 = −₹1,500
The trader won:
90% of the trades
and still lost money overall.
This demonstrates why win rate alone is incomplete.
But the opposite mistake is also common.
A low win rate combined with a large planned target does not automatically produce profits either.
You need the complete distribution of actual wins and losses.
Risk-Reward and Position Sizing
Risk-reward ratio tells you about the relationship between the planned loss and potential reward.
It does not tell you how many shares or contracts to trade.
That is where position sizing becomes important.
Suppose:
Entry = ₹500
Invalidation = ₹490
Risk per share:
₹10
Now suppose the trader’s predefined maximum planned loss for the setup is:
₹1,000
A simplified position-size calculation is:
Position Size = Maximum Planned Loss ÷ Risk per Share
Therefore:
₹1,000 ÷ ₹10 = 100 shares
This is only an educational illustration.
Actual position sizing should also consider factors such as:
- Account size
- Liquidity
- Slippage
- Gaps
- Portfolio exposure
- Correlation
- Leverage
- Product-specific risks
There is no universal percentage or rupee amount that is appropriate for every trader.
Risk-Reward Ratio Does Not Determine Account Risk
Consider two traders using the same setup:
Entry = ₹1,000
Stop = ₹980
Target = ₹1,040
Both have:
1:2 planned risk-reward
But:
Trader A
Buys 10 shares.
Risk:
10 × ₹20 = ₹200
Trader B
Buys 500 shares.
Risk:
500 × ₹20 = ₹10,000
The risk-reward ratio is identical.
The account-level risk is completely different.
That is why traders should distinguish:
Trade Structure
from:
Position Size
from:
Portfolio Risk
Where Should the Stop-Loss Come From?
A common mistake is deciding:
“I want a 1:2 trade.”
and then placing arbitrary stop and target distances solely to create that ratio.
The market does not know what ratio you want.
A better process is:
Setup → Logical Invalidation → Potential Target → Calculate R:R → Decide Whether Setup Fits the Strategy
The invalidation level should relate to the reason the trade exists.
Depending on the strategy, this could involve:
- Market structure
- Swing highs or lows
- Support or resistance
- Volatility
- Breakout failure
- Another predefined strategy condition
The purpose is not to make the stop artificially tight so the ratio looks attractive.
Risk-Reward and Support & Resistance
Suppose a stock is trading at ₹1,000.
Technical analysis identifies:
Important support near ₹970
and:
Important resistance near ₹1,040
If a long setup requires invalidation below ₹970, the potential risk may be larger than the available upside before resistance.
That information is useful.
The trader can conclude:
The setup does not currently fit the strategy’s required trade structure.
But there is no universal law saying every trader must reject every setup below 1:2.
Different strategies can have different historical win rates and payoff distributions.
The important question is whether the proposed trade fits the tested rules and historical characteristics of the strategy.
For a complete chart-analysis framework, read How to Do Technical Analysis for Stock Trading.
Can You Improve Risk-Reward by Using a Tighter Stop?
Mathematically, yes.
Practically, not necessarily.
Suppose:
Entry = ₹500
Target = ₹540
Stop A
₹480
Risk = ₹20
Reward = ₹40
R:R:
1:2
Stop B
₹495
Risk = ₹5
Reward = ₹40
R:R:
1:8
The second ratio looks dramatically better.
But if ₹495 lies inside ordinary market fluctuation, the trade may be stopped frequently before the intended setup has enough room to develop.
A better-looking ratio does not necessarily mean a better trade.
The stop should be connected to the trading logic rather than manipulated purely to improve a mathematical ratio.
Can You Improve Risk-Reward by Setting a Bigger Target?
Again, only on paper.
Suppose the nearest major resistance is around ₹1,050.
If a trader chooses ₹1,200 as the target simply to manufacture a larger reward/risk number, the target may not be realistic within the strategy’s timeframe.
This is another reason to distinguish:
Possible mathematical target
from:
Evidence-based target
Risk-reward ratio is most useful when both the stop and target come from a coherent trading framework.
Does a Stop-Loss Guarantee a 1R Maximum Loss?
No.
If 1R is planned as ₹1,000, the realised loss can still exceed ₹1,000 because of:
- Price gaps
- Rapid volatility
- Poor liquidity
- Slippage
- Order rejection
- Platform problems
- Product-specific execution mechanics
A stop-related order is a risk-management tool.
It is not a guarantee that every trade will close at exactly the planned price.
This is another reason why:
Planned Risk ≠ Guaranteed Maximum Realised Loss
Drawdown Recovery Mathematics
Large percentage losses require proportionately larger gains to recover because the recovery begins from a smaller capital base.
| Account Drawdown | Gain Required to Return to Starting Capital |
|---|---|
| 10% | 11.1% |
| 20% | 25.0% |
| 25% | 33.3% |
| 30% | 42.9% |
| 40% | 66.7% |
| 50% | 100.0% |
Example
Suppose an account starts at:
₹1,00,000
A 50% loss leaves:
₹50,000
To return from ₹50,000 to ₹1,00,000 requires:
₹50,000 gain
Relative to the remaining ₹50,000:
₹50,000 ÷ ₹50,000 = 100%
This demonstrates why controlling account-level exposure matters.
However, disciplined risk sizing cannot guarantee that large drawdowns will never occur.
Multiple losses, correlated positions, leverage, gaps and market shocks can still produce significant losses.
Why Unplanned Averaging Down Can Break the Risk Plan
Averaging down is not automatically wrong in every investment or trading framework.
The important distinction is whether additional entries were planned before the trade.
Consider this example.
Initial plan:
Maximum risk = ₹1,000
The trader enters a losing position.
Instead of following the predefined plan, they add more capital simply because the price has fallen.
The total potential loss becomes:
₹2,500
Now the original risk framework has changed.
That is different from a strategy where:
- Entry 1 is predefined
- Entry 2 is predefined
- Total maximum exposure is predefined
- Final invalidation is predefined
before the first order is placed.
The risk comes from unplanned emotional escalation, not merely from the existence of multiple entries.
Risk-Reward in Intraday vs Swing Trading
Risk-reward principles can be applied across different trading horizons, but the appropriate ratio is strategy-dependent.
Intraday Trading
Intraday traders operate within shorter time windows.
Relevant considerations can include:
- Daily volatility
- Liquidity
- Bid-ask spread
- Transaction costs
- Time remaining in the session
- Execution speed
An extremely distant target may be unrealistic if the instrument normally moves only a limited amount during the remaining trading session.
Swing Trading
Swing traders hold positions for longer periods.
This may allow larger price movements to develop, but it also introduces risks such as:
- Overnight gaps
- Company announcements
- Global-market events
- Multi-day volatility
Neither trading style has one universally correct risk-reward ratio.
The ratio should reflect the actual characteristics of the strategy.
Risk-Reward and Prospect Theory
Trading decisions are not always purely mathematical.
Prospect theory, developed by Daniel Kahneman and Amos Tversky, examines how people make decisions involving gains and losses under uncertainty.
One important idea associated with this research is loss aversion: people may respond more strongly to losses than to comparable gains.
In trading, behavioural tendencies can sometimes appear as:
- Taking small profits too quickly
- Delaying the acceptance of losses
- Moving a stop after entry
- Increasing risk to recover a previous loss
These behaviours can materially change the realised risk-reward profile.
For example:
Planned winner = +2R
may become:
Realised winner = +0.7R
because the trader exits early.
Meanwhile:
Planned loss = −1R
may become:
Realised loss = −1.8R
because the trader refuses to follow the original invalidation.
The resulting strategy can be very different from the one originally designed.
For a deeper behavioural framework, read How to Avoid Emotional Trading Mistakes.
Why a Trading Journal Matters
You cannot properly evaluate risk-reward from memory alone.
A useful trading journal can record:
- Entry
- Planned stop
- Planned target
- Planned risk-reward
- Position size
- 1R amount
- Actual exit
- Realised R-multiple
- Trading costs
- Setup
- Market condition
- Reason for deviation
After a sufficiently meaningful sample, compare:
Planned average R:R
with:
Realised average win/loss
You may discover that a strategy designed around 1:2 actually produces:
Average win = +1.35R
and:
Average loss = −1.05R
That is the data you should use when analysing expectancy.
Sample Risk-Reward Journal
| Trade | Planned R:R | Result | Realised R |
|---|---|---|---|
| 1 | 1:2 | Loss | -1R |
| 2 | 1:2 | Win | +1.6R |
| 3 | 1:3 | Loss | -0.8R |
| 4 | 1:2 | Win | +2R |
| 5 | 1:2.5 | Win | +1.1R |
| 6 | 1:2 | Loss | -1.2R |
Notice that the realised result does not always equal the planned target or stop.
That is normal.
The objective of the journal is to measure what actually happened.
Common Risk-Reward Ratio Mistakes
Believing 1:2 Guarantees Profit
It does not.
Win rate and realised outcomes matter.
Chasing the Highest Possible Ratio
A 1:10 target is not automatically better than 1:2 if it is almost never reached.
Placing an Arbitrarily Tight Stop
A tighter stop improves the displayed R:R but may not fit the market structure.
Setting Unrealistic Targets
A distant target can make the ratio look attractive while reducing the probability of reaching it.
Ignoring Trading Costs
Gross expectancy can differ materially from net expectancy.
Ignoring Slippage
Actual exits may differ from planned exits.
Focusing Only on Win Rate
A high win rate can still produce losses if average losses are much larger than average wins.
Ignoring Position Size
Two identical 1:2 setups can create completely different account risk.
Moving the Stop Emotionally
Changing the invalidation after entry can increase the realised loss.
Taking Profits Too Early
Repeatedly exiting winners before their planned target can reduce the realised average win.
Evaluating Too Few Trades
Five or ten trades may not provide enough information to understand how a strategy behaves across different market conditions.
A Practical Risk-Reward Checklist
Before entering a trade, ask:
Setup
- What is the reason for the trade?
- What market condition is present?
- Does the setup meet predefined rules?
Invalidation
- What specific condition makes the idea wrong?
- Is the stop linked to the setup rather than an arbitrary percentage?
Reward
- Where is the potential target?
- Is that target realistic based on market structure and volatility?
Ratio
- What is the planned risk-reward ratio?
- Am I using risk:reward or reward:risk terminology correctly?
Position Size
- How much money is actually at risk?
- Does the position size fit the account-level risk framework?
Execution
- Is liquidity sufficient?
- Could slippage materially change the result?
- What trading costs apply?
Strategy Data
- What is the historical win rate?
- What is the actual average winner?
- What is the actual average loser?
- What is the historical expectancy after costs?
Behaviour
- Am I likely to move the stop?
- Do I frequently exit winners early?
- Am I increasing risk because of a previous loss?
A ratio should be evaluated within this complete framework.
Frequently Asked Questions
What is risk-reward ratio in trading?
Risk-reward ratio compares the potential amount a trader plans to lose if a trade is invalidated with the potential amount the trader may gain if the target is reached.
What does a 1:2 risk-reward ratio mean?
Using the risk:reward convention in this article, 1:2 means risking one unit to pursue a potential reward of two units.
For example:
₹1,000 planned risk → ₹2,000 potential reward
What does a 1:3 risk-reward ratio mean?
It means one unit of planned risk compared with three units of potential reward.
For example:
₹500 risk → ₹1,500 potential reward
Is 1:2 the best risk-reward ratio?
No.
There is no universally best risk-reward ratio.
The appropriate relationship depends on the strategy’s win rate, market structure, timeframe, costs, volatility and realised historical performance.
Is 1:1 risk-reward bad?
Not necessarily.
A strategy with an average 1:1 payoff could theoretically have positive expectancy if its win rate is sufficiently above the breakeven level after costs.
Can I be profitable with a low win rate?
Theoretically, yes, if average winning trades are sufficiently larger than average losing trades and the overall strategy retains positive expectancy after costs.
A low win rate by itself does not guarantee profitability.
Can I lose money with a high win rate?
Yes.
A trader can win frequently and still lose overall if occasional losses are much larger than average winners.
What is the breakeven win rate for 1:2 risk-reward?
Under simplified assumptions of an average +2R winner and −1R loser, the theoretical breakeven win rate is approximately:
33.33% before trading costs
Real-world costs increase the required breakeven performance.
What is the breakeven win rate for 1:3?
With an average +3R winner and −1R loser:
1 ÷ 4 = 25%
before costs.
What is R in trading?
R represents one unit of predefined risk.
If the amount planned to be lost when the trade reaches invalidation is ₹1,000:
1R = ₹1,000
What is expectancy in trading?
Expectancy estimates the average result of a strategy based on its win rate, loss rate, average winners and average losers.
A simplified formula is:
Expectancy = (Win Rate × Average Win) − (Loss Rate × Average Loss)
Does positive expectancy guarantee future profit?
No.
Expectancy calculated from historical data does not guarantee future performance because market conditions and strategy behaviour can change.
Does a stop-loss guarantee my maximum loss?
No.
Slippage, gaps, liquidity and execution conditions can cause a realised loss to differ from the planned stop level.
Should I always target twice my stop-loss?
No.
Targets and invalidation levels should come from the strategy and market structure rather than being forced to create a particular ratio.
Does risk-reward ratio matter for intraday trading?
Yes, it can help evaluate the relationship between planned risk and potential reward, but intraday traders also need to consider liquidity, volatility, time, execution and transaction costs.
Does risk-reward matter for swing trading?
Yes.
Swing traders can use the same principles, while also considering overnight gaps and multi-day market risk.
Can risk-reward ratio predict whether a trade will win?
No.
It describes the proposed payoff structure.
It does not predict market direction or the probability that the target will be reached.
What matters more: win rate or risk-reward ratio?
Neither should be evaluated alone.
The relationship between:
Win Rate + Average Winner + Average Loser + Costs
determines the strategy’s expectancy.
Final Thoughts
Risk-reward ratio is useful because it forces a trader to think about potential loss before focusing only on potential profit.
But it is not a secret formula for profitable trading.
A planned 1:2 or 1:3 ratio means little if:
- The target is unrealistic
- The stop is arbitrary
- The trader exits winners early
- Losses exceed the planned stop
- Position sizing is inconsistent
- Trading costs remove the edge
- The strategy’s win rate is too low
The stronger framework is:
Setup → Invalidation → Potential Reward → Risk-Reward Ratio → Position Size → Execution → Realised R → Expectancy
Remember:
High Win Rate ≠ Guaranteed Profit
High Risk-Reward Ratio ≠ Guaranteed Profit
Planned R:R ≠ Realised R:R
Stop-Loss ≠ Guaranteed Exit Price
Positive Historical Expectancy ≠ Guaranteed Future Results
The goal is not to find the largest possible reward-to-risk number.
The goal is to understand whether your strategy produces a sustainable relationship between:
how often it wins
how much it makes when right
and:
how much it loses when wrong
after real trading costs and execution are included.
For a broader risk-management framework, continue with How to Manage Risk in the Indian Stock Market.
For help identifying logical invalidation and target areas from price structure, read How to Do Technical Analysis for Stock Trading.
For behavioural mistakes that can change planned risk after entry, read How to Avoid Emotional Trading Mistakes.
Educational Disclaimer: This article is for general educational and informational purposes only. It does not constitute investment, financial, research or trading advice. Trading involves risk, including possible loss of capital. Risk-reward ratios, stop-losses, position sizing, technical analysis, backtesting and positive historical expectancy cannot guarantee profitable results or limit every loss to a predetermined amount.
