risk-reward-ratio

Name: risk-reward-ratio
Author: SKE-Labs

15stars2forks

How to install risk-reward-ratio?

ske-labs/agent-trading-skills/risk-reward-ratio

$npx -y skills add SKE-Labs/agent-trading-skills --skill risk-reward-ratio

Installs into the current project.

Summary

Calculate long/short planned R-multiples and net expectancy. Use when evaluating executable entry, stop, target, win/loss distribution, costs, gaps, and uncertainty without treating R:R as trade quality by itself.

Files · 1

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SKILL.md

1	# Risk-Reward Ratio (R:R)
2
3	R:R compares potential profit to potential loss, helping filter high-quality trades.
4
5	## Calculation
6
7	For a long: `R:R = (Target - Entry) / (Entry - Stop)`. For a short: `R:R = (Entry - Target) / (Stop - Entry)`. Require positive denominators and use executable prices plus expected slippage/fees.
8
9	Example: Entry $100, Stop $95, Target $115 => R:R = $15 / $5 = 3:1.
10
11	The zero-cost binary breakeven R:R is `(1 - Win Rate) / Win Rate`. Actual breakeven is higher after costs, gaps, partial fills, and non-binary exits.
12
13	\| Win Rate \| Minimum R:R \| Breakeven R:R \|
14	\| -------- \| ----------- \| ------------- \|
15	\| 40% \| 1.5:1 \| 1.5:1 \|
16	\| 50% \| 1:1 \| 1:1 \|
17	\| 60% \| 0.7:1 \| 0.67:1 \|
18	\| 70% \| 0.5:1 \| 0.43:1 \|
19
20	## Expectancy
21
22	Use realized net payoff distributions: `E = p × average_win - (1-p) × average_loss - average_costs`. Report uncertainty, tail loss, sample period, and regime stability. Planned R:R alone does not establish win probability or trade quality.
23
24	## Optimizing R:R
25
26	Improve Entry: Enter at better levels (OTE, pullbacks), wait for confirmation at S/R, use limit orders at key levels.
27
28	Define Stop: use thesis invalidation plus a validated tick/volatility buffer, then size from the resulting risk.
29
30	Define Target: use observable structure, time exit, or a tested rule; moving a target farther away improves displayed R:R but can reduce hit probability.
31
32	Filter on conservative net expectancy, tail risk, liquidity, and mandate fit. Do not label a trade good or excellent from its planned R:R.
33
34	## Workflow
35
36	1. Identify entry from technical analysis
37	2. Set stop loss based on structure or ATR (see stop-loss-strategies)
38	3. Calculate R:R using the formula above
39	4. Filter -- require positive conservative net expectancy and mandate compliance
40	5. Set targets at R:R milestones (1R, 2R, 3R) for partial exits
41
42	## Evidence and Validation
43
44	- Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
45	- Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
46	- Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
47	- Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
48	- Research basis: R-multiples describe a planned payoff but do not establish expectancy. Include win probability, costs, gaps, and partial fills; [FINRA](https://www.finra.org/investors/investing/investing-basics/fees-commissions) notes that trading costs persist even with zero commissions.
49
50	## Key Rules
51
52	- Evaluate win probability and payoff jointly; neither high R:R nor high win rate is sufficient.
53	- Use executable, predeclared targets and stops rather than decorative ratios.
54	- Avoid selection bias from waiting only for entries that make the displayed ratio attractive.
55	- Return `no trade` when net expectancy, uncertainty, liquidity, or risk limits fail.
56
57	## Related Skills
58
59	- position-sizing -- R:R determines trade quality; position sizing determines trade quantity
60	- stop-loss-strategies -- stop placement defines the risk side of the R:R equation

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risk-reward-ratio

15stars2forks

View on GitHub

How to install risk-reward-ratio?

ske-labs/agent-trading-skills/risk-reward-ratio

$npx -y skills add SKE-Labs/agent-trading-skills --skill risk-reward-ratio

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Risk-Reward Ratio (R:R)
2
3	R:R compares potential profit to potential loss, helping filter high-quality trades.
4
5	## Calculation
6
7	For a long: `R:R = (Target - Entry) / (Entry - Stop)`. For a short: `R:R = (Entry - Target) / (Stop - Entry)`. Require positive denominators and use executable prices plus expected slippage/fees.
8
9	Example: Entry $100, Stop $95, Target $115 => R:R = $15 / $5 = 3:1.
10
11	The zero-cost binary breakeven R:R is `(1 - Win Rate) / Win Rate`. Actual breakeven is higher after costs, gaps, partial fills, and non-binary exits.
12
13	\| Win Rate \| Minimum R:R \| Breakeven R:R \|
14	\| -------- \| ----------- \| ------------- \|
15	\| 40% \| 1.5:1 \| 1.5:1 \|
16	\| 50% \| 1:1 \| 1:1 \|
17	\| 60% \| 0.7:1 \| 0.67:1 \|
18	\| 70% \| 0.5:1 \| 0.43:1 \|
19
20	## Expectancy
21
22	Use realized net payoff distributions: `E = p × average_win - (1-p) × average_loss - average_costs`. Report uncertainty, tail loss, sample period, and regime stability. Planned R:R alone does not establish win probability or trade quality.
23
24	## Optimizing R:R
25
26	Improve Entry: Enter at better levels (OTE, pullbacks), wait for confirmation at S/R, use limit orders at key levels.
27
28	Define Stop: use thesis invalidation plus a validated tick/volatility buffer, then size from the resulting risk.
29
30	Define Target: use observable structure, time exit, or a tested rule; moving a target farther away improves displayed R:R but can reduce hit probability.
31
32	Filter on conservative net expectancy, tail risk, liquidity, and mandate fit. Do not label a trade good or excellent from its planned R:R.
33
34	## Workflow
35
36	1. Identify entry from technical analysis
37	2. Set stop loss based on structure or ATR (see stop-loss-strategies)
38	3. Calculate R:R using the formula above
39	4. Filter -- require positive conservative net expectancy and mandate compliance
40	5. Set targets at R:R milestones (1R, 2R, 3R) for partial exits
41
42	## Evidence and Validation
43
44	- Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
45	- Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
46	- Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
47	- Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
48	- Research basis: R-multiples describe a planned payoff but do not establish expectancy. Include win probability, costs, gaps, and partial fills; [FINRA](https://www.finra.org/investors/investing/investing-basics/fees-commissions) notes that trading costs persist even with zero commissions.
49
50	## Key Rules
51
52	- Evaluate win probability and payoff jointly; neither high R:R nor high win rate is sufficient.
53	- Use executable, predeclared targets and stops rather than decorative ratios.
54	- Avoid selection bias from waiting only for entries that make the displayed ratio attractive.
55	- Return `no trade` when net expectancy, uncertainty, liquidity, or risk limits fail.
56
57	## Related Skills
58
59	- position-sizing -- R:R determines trade quality; position sizing determines trade quantity
60	- stop-loss-strategies -- stop placement defines the risk side of the R:R equation

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6 picks

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