position-sizing

Name: position-sizing
Author: agiprolabs

242installs53forks

How to install position-sizing?

agiprolabs/claude-trading-skills/position-sizing

$npx -y skills add agiprolabs/claude-trading-skills --skill position-sizing

Installs into the current project.

Summary

Trade sizing methods including fixed fractional, volatility-adjusted, Kelly criterion, and liquidity-constrained sizing

Files · 1

View on GitHub

SKILL.md

1	# Position Sizing
2
3	Position sizing is the single most important risk management decision in trading. Your entry signal determines direction; your position size determines survival. A mediocre strategy with proper sizing will outperform a great strategy with reckless sizing over any meaningful time horizon.
4
5	Core principle: Size determines survival, not entries. Two traders with the same signals but different sizing will have wildly different outcomes. The one who sizes conservatively survives drawdowns and compounds capital; the one who oversizes blows up.
6
7	## Methods Covered
8
9	\| Method \| Best For \| Key Input \|
10	\|--------\|----------\|-----------\|
11	\| Fixed Fractional \| General trading, most recommended \| Account risk % \|
12	\| Volatility-Adjusted \| Volatile markets, multi-asset \| ATR or realized vol \|
13	\| Kelly Criterion \| Quantified edge with track record \| Win rate + payoff ratio \|
14	\| Liquidity-Constrained \| Low-liquidity Solana tokens \| Pool depth \|
15	\| Anti-Martingale \| Trend-following strategies \| Recent P&L streak \|
16
17	---
18
19	## 1. Fixed Fractional Sizing
20
21	The most recommended method for most traders. Risk a fixed percentage of your account on each trade.
22
23	### Formula
24
25	```
26	risk_amount = account_value * risk_percentage
27	price_risk_per_unit = entry_price - stop_loss_price
28	position_size_units = risk_amount / price_risk_per_unit
29	position_value = position_size_units * entry_price
30	```
31
32	### Risk Tiers
33
34	\| Tier \| Risk Per Trade \| Use Case \|
35	\|------\|---------------\|----------\|
36	\| Conservative \| 0.5–1% \| New strategies, drawdown recovery \|
37	\| Standard \| 1–2% \| Most traders, proven strategies \|
38	\| Aggressive \| 3–5% \| High-conviction setups with strong, measured edge \|
39
40	### Example
41
42	```python
43	account = 10_000 # $10,000 or 100 SOL
44	risk_pct = 0.02 # 2%
45	entry = 1.50
46	stop_loss = 1.30
47
48	risk_amount = account * risk_pct # $200
49	price_risk = entry - stop_loss # $0.20
50	position_units = risk_amount / price_risk # 1,000 tokens
51	position_value = position_units * entry # $1,500
52	```
53
54	With this sizing, if the stop loss is hit, you lose exactly 2% of your account regardless of the token's price or volatility.
55
56	---
57
58	## 2. Volatility-Adjusted Sizing
59
60	Scale position size inversely with volatility. When volatility is high, take smaller positions; when low, take larger positions. This normalizes the dollar risk across different market conditions.
61
62	### Formula
63
64	```
65	adjusted_size = base_size * (target_vol / current_vol)
66	```
67
68	Where:
69	- `target_vol`: your desired daily portfolio volatility (e.g., 2%)
70	- `current_vol`: the token's current daily volatility (from ATR or realized vol)
71
72	### Using ATR
73
74	```python
75	atr_14 = 0.12 # 14-period ATR
76	close_price = 1.50
77	daily_vol_pct = atr_14 / close_price # 8%
78
79	target_daily_vol = account * 0.02 # $200 target daily move
80	position_size = target_daily_vol / atr_14 # 1,667 units
81	```
82
83	This automatically reduces exposure in volatile markets and increases it in calm ones.
84
85	---
86
87	## 3. Kelly Criterion
88
89	The mathematically optimal fraction of capital to risk, maximizing long-term growth rate. Derived from maximizing expected logarithmic utility.
90
91	### Formula
92
93	```
94	f* = (p * b - q) / b
95	```
96
97	Where:
98	- `p` = win rate (probability of winning trade)
99	- `q` = 1 - p (probability of losing trade)
100	- `b` = average win / average loss (payoff ratio)
101	- `f*` = optimal fraction of capital to risk
102
103	Equivalent form: `f* = (p * (b + 1) - 1) / b`
104
105	### Critical Rule: NEVER Use Full Kelly
106
107	Full Kelly assumes perfect knowledge of your edge. In practice, edge estimates are noisy. Always use fractional Kelly:
108
109	\| Fraction \| Use Case \| Notes \|
110	\|----------\|----------\|-------\|
111	\| 0.25x Kelly \| Conservative, recommended default \| Robust to edge estimation error \|
112	\| 0.50x Kelly \| Moderate, for well-measured edges \| Still significant drawdown risk \|
113	\| 1.0x Kelly \| Never in practice \| Theoretical maximum, catastrophic if edge is overestimated \|
114
115	### Example
116
117	```python
118	win_rate = 0.55 # 55% win rate
119	avg_win = 2.0 # Average win is 2x the average loss
120	avg_loss = 1.0
121	payoff_ratio = avg_win / avg_loss # b = 2.0
122
123	kelly = (win_rate * payoff_ratio - (1 - win_rate)) / payoff_ratio
124	# kelly = (0.55 * 2.0 - 0.45) / 2.0 = 0.325 = 32.5%
125
126	quarter_kelly = kelly * 0.25 # 8.1% — use this
127	half_kelly = kelly * 0.50 # 16.25%
128	```
129
130	If Kelly is negative, you have no edge. Do not trade.
131
132	See `references/sizing_formulas.md` for the full mathematical derivation.
133
134	---
135
136	## 4. Liquidity-Constrained Sizing
137
138	Critical for Solana tokens. Even if your risk model says you can take a large position, the pool may not support it without unacceptable slippage.
139
140	### Formula (Constant-Product AMM)
141
142	```
143	slippage ≈ trade_size / pool_liquidity
144	max_trade = pool_liquidity * max_slippage_pct
145	```
146
147	### Rules of Thumb
148
149	\| Constraint \| Guideline \|
150	\|-----------\|-----------\|
151	\| Max single trade \| 2% of

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position-sizing

242installs53forks

View on GitHub

How to install position-sizing?

agiprolabs/claude-trading-skills/position-sizing

$npx -y skills add agiprolabs/claude-trading-skills --skill position-sizing

Installs into the current project.

Summary

Trade sizing methods including fixed fractional, volatility-adjusted, Kelly criterion, and liquidity-constrained sizing

Files · 1

View on GitHub

SKILL.md

1	# Position Sizing
2
3	Position sizing is the single most important risk management decision in trading. Your entry signal determines direction; your position size determines survival. A mediocre strategy with proper sizing will outperform a great strategy with reckless sizing over any meaningful time horizon.
4
5	Core principle: Size determines survival, not entries. Two traders with the same signals but different sizing will have wildly different outcomes. The one who sizes conservatively survives drawdowns and compounds capital; the one who oversizes blows up.
6
7	## Methods Covered
8
9	\| Method \| Best For \| Key Input \|
10	\|--------\|----------\|-----------\|
11	\| Fixed Fractional \| General trading, most recommended \| Account risk % \|
12	\| Volatility-Adjusted \| Volatile markets, multi-asset \| ATR or realized vol \|
13	\| Kelly Criterion \| Quantified edge with track record \| Win rate + payoff ratio \|
14	\| Liquidity-Constrained \| Low-liquidity Solana tokens \| Pool depth \|
15	\| Anti-Martingale \| Trend-following strategies \| Recent P&L streak \|
16
17	---
18
19	## 1. Fixed Fractional Sizing
20
21	The most recommended method for most traders. Risk a fixed percentage of your account on each trade.
22
23	### Formula
24
25	```
26	risk_amount = account_value * risk_percentage
27	price_risk_per_unit = entry_price - stop_loss_price
28	position_size_units = risk_amount / price_risk_per_unit
29	position_value = position_size_units * entry_price
30	```
31
32	### Risk Tiers
33
34	\| Tier \| Risk Per Trade \| Use Case \|
35	\|------\|---------------\|----------\|
36	\| Conservative \| 0.5–1% \| New strategies, drawdown recovery \|
37	\| Standard \| 1–2% \| Most traders, proven strategies \|
38	\| Aggressive \| 3–5% \| High-conviction setups with strong, measured edge \|
39
40	### Example
41
42	```python
43	account = 10_000 # $10,000 or 100 SOL
44	risk_pct = 0.02 # 2%
45	entry = 1.50
46	stop_loss = 1.30
47
48	risk_amount = account * risk_pct # $200
49	price_risk = entry - stop_loss # $0.20
50	position_units = risk_amount / price_risk # 1,000 tokens
51	position_value = position_units * entry # $1,500
52	```
53
54	With this sizing, if the stop loss is hit, you lose exactly 2% of your account regardless of the token's price or volatility.
55
56	---
57
58	## 2. Volatility-Adjusted Sizing
59
60	Scale position size inversely with volatility. When volatility is high, take smaller positions; when low, take larger positions. This normalizes the dollar risk across different market conditions.
61
62	### Formula
63
64	```
65	adjusted_size = base_size * (target_vol / current_vol)
66	```
67
68	Where:
69	- `target_vol`: your desired daily portfolio volatility (e.g., 2%)
70	- `current_vol`: the token's current daily volatility (from ATR or realized vol)
71
72	### Using ATR
73
74	```python
75	atr_14 = 0.12 # 14-period ATR
76	close_price = 1.50
77	daily_vol_pct = atr_14 / close_price # 8%
78
79	target_daily_vol = account * 0.02 # $200 target daily move
80	position_size = target_daily_vol / atr_14 # 1,667 units
81	```
82
83	This automatically reduces exposure in volatile markets and increases it in calm ones.
84
85	---
86
87	## 3. Kelly Criterion
88
89	The mathematically optimal fraction of capital to risk, maximizing long-term growth rate. Derived from maximizing expected logarithmic utility.
90
91	### Formula
92
93	```
94	f* = (p * b - q) / b
95	```
96
97	Where:
98	- `p` = win rate (probability of winning trade)
99	- `q` = 1 - p (probability of losing trade)
100	- `b` = average win / average loss (payoff ratio)
101	- `f*` = optimal fraction of capital to risk
102
103	Equivalent form: `f* = (p * (b + 1) - 1) / b`
104
105	### Critical Rule: NEVER Use Full Kelly
106
107	Full Kelly assumes perfect knowledge of your edge. In practice, edge estimates are noisy. Always use fractional Kelly:
108
109	\| Fraction \| Use Case \| Notes \|
110	\|----------\|----------\|-------\|
111	\| 0.25x Kelly \| Conservative, recommended default \| Robust to edge estimation error \|
112	\| 0.50x Kelly \| Moderate, for well-measured edges \| Still significant drawdown risk \|
113	\| 1.0x Kelly \| Never in practice \| Theoretical maximum, catastrophic if edge is overestimated \|
114
115	### Example
116
117	```python
118	win_rate = 0.55 # 55% win rate
119	avg_win = 2.0 # Average win is 2x the average loss
120	avg_loss = 1.0
121	payoff_ratio = avg_win / avg_loss # b = 2.0
122
123	kelly = (win_rate * payoff_ratio - (1 - win_rate)) / payoff_ratio
124	# kelly = (0.55 * 2.0 - 0.45) / 2.0 = 0.325 = 32.5%
125
126	quarter_kelly = kelly * 0.25 # 8.1% — use this
127	half_kelly = kelly * 0.50 # 16.25%
128	```
129
130	If Kelly is negative, you have no edge. Do not trade.
131
132	See `references/sizing_formulas.md` for the full mathematical derivation.
133
134	---
135
136	## 4. Liquidity-Constrained Sizing
137
138	Critical for Solana tokens. Even if your risk model says you can take a large position, the pool may not support it without unacceptable slippage.
139
140	### Formula (Constant-Product AMM)
141
142	```
143	slippage ≈ trade_size / pool_liquidity
144	max_trade = pool_liquidity * max_slippage_pct
145	```
146
147	### Rules of Thumb
148
149	\| Constraint \| Guideline \|
150	\|-----------\|-----------\|
151	\| Max single trade \| 2% of

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