trade-journal

Name: trade-journal
Author: agiprolabs

235stars53forks

How to install trade-journal?

agiprolabs/claude-trading-skills/trade-journal

$npx -y skills add agiprolabs/claude-trading-skills --skill trade-journal

Installs into the current project.

Summary

Structured trade logging, performance review, behavioral pattern detection, and strategy attribution for systematic improvement

Files · 1

View on GitHub

SKILL.md

1	# Trade Journal
2
3	Structured trade journaling for systematic improvement. Log every trade with context, review performance at multiple cadences, detect behavioral patterns that destroy edge, and attribute returns to specific strategies.
4
5	## Why Journaling Matters
6
7	Most traders fail not from bad strategies but from bad behavior. A trade journal transforms subjective "feel" into objective data:
8
9	- Strategy Attribution: Know which setups actually make money vs. which feel profitable
10	- Behavioral Detection: Catch revenge trading, FOMO entries, and premature exits before they compound
11	- Pattern Recognition: Discover that your Monday morning trades lose money, or that you cut SOL winners too early
12	- Accountability: Written rationale before entry forces deliberate decision-making
13	- Improvement Tracking: Measure whether changes to your process actually improve results
14
15	Without a journal, you optimize on noise. With one, you optimize on signal.
16
17	## Trade Record Structure
18
19	Every trade record captures context at entry and outcome at exit. See `references/record_format.md` for the complete 18-field schema.
20
21	### Minimum Required Fields
22
23	```python
24	trade = {
25	"id": "T-20250310-001",
26	"token": "SOL",
27	"direction": "long",
28	"entry_date": "2025-03-10T14:30:00Z",
29	"entry_price": 142.50,
30	"size_sol": 5.0,
31	"strategy": "momentum-breakout",
32	"rationale": "Breaking above 4h resistance at 141.80 with volume confirmation",
33	"exit_date": "2025-03-10T16:45:00Z",
34	"exit_price": 146.20,
35	"pnl_sol": 0.648,
36	"outcome": "win",
37	"lessons": "Held through initial pullback to 143.0, rewarded for patience"
38	}
39	```
40
41	### Strategy Tagging
42
43	Use consistent tags to enable performance attribution:
44
45	\| Category \| Tags \|
46	\|----------\|------\|
47	\| Momentum \| `momentum-breakout`, `trend-continuation`, `pullback-entry` \|
48	\| Mean Reversion \| `range-fade`, `oversold-bounce`, `deviation-snap` \|
49	\| Event-Driven \| `listing-play`, `catalyst-trade`, `news-reaction` \|
50	\| On-Chain \| `whale-follow`, `wallet-copy`, `flow-signal` \|
51	\| DeFi \| `lp-entry`, `yield-farm`, `arb-capture` \|
52
53	### Rationale Templates
54
55	Write rationale before entering. Templates by setup type:
56
57	```
58	Momentum: "[Token] breaking [level] on [timeframe] with [confirmation]. Target [price], stop [price]."
59	Mean Reversion: "[Token] at [X] std devs from [mean] on [timeframe]. Expecting reversion to [target]."
60	On-Chain: "[Signal type] detected — [wallet/flow description]. Historical hit rate [X]%."
61	```
62
63	## Storage Format
64
65	The journal uses JSON for structured querying and CSV for spreadsheet compatibility.
66
67	### JSON Format (Primary)
68
69	```json
70	{
71	"journal_version": "1.0",
72	"trader_id": "anon",
73	"trades": [
74	{
75	"id": "T-20250310-001",
76	"token": "SOL",
77	"direction": "long",
78	"entry_date": "2025-03-10T14:30:00Z",
79	"entry_price": 142.50,
80	"size_sol": 5.0,
81	"size_usd": 712.50,
82	"strategy": "momentum-breakout",
83	"setup_quality": 8,
84	"rationale": "Breaking above 4h resistance with volume",
85	"exit_date": "2025-03-10T16:45:00Z",
86	"exit_price": 146.20,
87	"pnl_sol": 0.648,
88	"pnl_pct": 2.60,
89	"outcome": "win",
90	"hold_time_minutes": 135,
91	"emotional_state": "calm",
92	"lessons": "Patience through pullback paid off",
93	"tags": ["high-conviction", "clean-setup"]
94	}
95	]
96	}
97	```
98
99	### CSV Format (Export)
100
101	```
102	id,token,direction,entry_date,entry_price,size_sol,strategy,exit_date,exit_price,pnl_sol,pnl_pct,outcome,lessons
103	T-20250310-001,SOL,long,2025-03-10T14:30:00Z,142.50,5.0,momentum-breakout,2025-03-10T16:45:00Z,146.20,0.648,2.60,win,"Patience paid off"
104	```
105
106	## Analytics from Journal Data
107
108	### Win Rate by Strategy
109
110	```python
111	from collections import Counter
112
113	def win_rate_by_strategy(trades: list[dict]) -> dict[str, float]:
114	"""Compute win rate grouped by strategy tag."""
115	strategy_outcomes: dict[str, list[str]] = {}
116	for t in trades:
117	strat = t["strategy"]
118	strategy_outcomes.setdefault(strat, []).append(t["outcome"])
119
120	return {
121	strat: outcomes.count("win") / len(outcomes)
122	for strat, outcomes in strategy_outcomes.items()
123	if len(outcomes) >= 5 # minimum sample size
124	}
125	```
126
127	### Performance by Time of Day
128
129	```python
130	from datetime import datetime
131
132	def pnl_by_hour(trades: list[dict]) -> dict[int, float]:
133	"""Aggregate P&L by entry hour (UTC)."""
134	hourly: dict[int, float] = {}
135	for t in trades:
136	hour = datetime.fromisoformat(t["entry_date"].rstrip("Z")).hour
137	hourly[hour] = hourly.get(hour, 0.0) + t.get("pnl_sol", 0.0)
138	return dict(sorted(hourly.items()))
139	```
140
141	### Profit Factor by Token Type
142
143	```python
144	def profit_factor(trades: list[dict], group_key: str = "token") -> dict[str, float]:
145	"""Compute profit factor (gross wins / gross losses)

People who liked this also viewed

6 picks

Type

azure-costAzure cost management: query costs, forecast spending, optimize to reduce waste. WHEN: \"Azure costs\", \"Azure bill\", \"cost breakdown\", \"how much am I spending\", \"forecast spending\", \"optimizSkillsJul 2026275k1.3k
pricingWhen the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increaseSkillsJul 202638k41k
stripe-integrationMulti-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLISkillsJul 202611k38k
startup-financial-modelingMulti-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLISkillsJul 20269.5k38k
tushare-finance专为 Claude Code 设计的 Tushare Pro 金融数据技能包，支持 220+ 个接口，用自然语言获取 A 股行情、财务数据、宏观经济等金融数据。SkillsJul 20265.0k75
azure-pricingFetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com) and estimates Copilot Studio agent credit consumption. Use when the user asks about the cost of any Azure seSkillsJul 20264.5k37k

trade-journal

235stars53forks

View on GitHub

How to install trade-journal?

agiprolabs/claude-trading-skills/trade-journal

$npx -y skills add agiprolabs/claude-trading-skills --skill trade-journal

Installs into the current project.

Summary

Structured trade logging, performance review, behavioral pattern detection, and strategy attribution for systematic improvement

Files · 1

View on GitHub

SKILL.md

1	# Trade Journal
2
3	Structured trade journaling for systematic improvement. Log every trade with context, review performance at multiple cadences, detect behavioral patterns that destroy edge, and attribute returns to specific strategies.
4
5	## Why Journaling Matters
6
7	Most traders fail not from bad strategies but from bad behavior. A trade journal transforms subjective "feel" into objective data:
8
9	- Strategy Attribution: Know which setups actually make money vs. which feel profitable
10	- Behavioral Detection: Catch revenge trading, FOMO entries, and premature exits before they compound
11	- Pattern Recognition: Discover that your Monday morning trades lose money, or that you cut SOL winners too early
12	- Accountability: Written rationale before entry forces deliberate decision-making
13	- Improvement Tracking: Measure whether changes to your process actually improve results
14
15	Without a journal, you optimize on noise. With one, you optimize on signal.
16
17	## Trade Record Structure
18
19	Every trade record captures context at entry and outcome at exit. See `references/record_format.md` for the complete 18-field schema.
20
21	### Minimum Required Fields
22
23	```python
24	trade = {
25	"id": "T-20250310-001",
26	"token": "SOL",
27	"direction": "long",
28	"entry_date": "2025-03-10T14:30:00Z",
29	"entry_price": 142.50,
30	"size_sol": 5.0,
31	"strategy": "momentum-breakout",
32	"rationale": "Breaking above 4h resistance at 141.80 with volume confirmation",
33	"exit_date": "2025-03-10T16:45:00Z",
34	"exit_price": 146.20,
35	"pnl_sol": 0.648,
36	"outcome": "win",
37	"lessons": "Held through initial pullback to 143.0, rewarded for patience"
38	}
39	```
40
41	### Strategy Tagging
42
43	Use consistent tags to enable performance attribution:
44
45	\| Category \| Tags \|
46	\|----------\|------\|
47	\| Momentum \| `momentum-breakout`, `trend-continuation`, `pullback-entry` \|
48	\| Mean Reversion \| `range-fade`, `oversold-bounce`, `deviation-snap` \|
49	\| Event-Driven \| `listing-play`, `catalyst-trade`, `news-reaction` \|
50	\| On-Chain \| `whale-follow`, `wallet-copy`, `flow-signal` \|
51	\| DeFi \| `lp-entry`, `yield-farm`, `arb-capture` \|
52
53	### Rationale Templates
54
55	Write rationale before entering. Templates by setup type:
56
57	```
58	Momentum: "[Token] breaking [level] on [timeframe] with [confirmation]. Target [price], stop [price]."
59	Mean Reversion: "[Token] at [X] std devs from [mean] on [timeframe]. Expecting reversion to [target]."
60	On-Chain: "[Signal type] detected — [wallet/flow description]. Historical hit rate [X]%."
61	```
62
63	## Storage Format
64
65	The journal uses JSON for structured querying and CSV for spreadsheet compatibility.
66
67	### JSON Format (Primary)
68
69	```json
70	{
71	"journal_version": "1.0",
72	"trader_id": "anon",
73	"trades": [
74	{
75	"id": "T-20250310-001",
76	"token": "SOL",
77	"direction": "long",
78	"entry_date": "2025-03-10T14:30:00Z",
79	"entry_price": 142.50,
80	"size_sol": 5.0,
81	"size_usd": 712.50,
82	"strategy": "momentum-breakout",
83	"setup_quality": 8,
84	"rationale": "Breaking above 4h resistance with volume",
85	"exit_date": "2025-03-10T16:45:00Z",
86	"exit_price": 146.20,
87	"pnl_sol": 0.648,
88	"pnl_pct": 2.60,
89	"outcome": "win",
90	"hold_time_minutes": 135,
91	"emotional_state": "calm",
92	"lessons": "Patience through pullback paid off",
93	"tags": ["high-conviction", "clean-setup"]
94	}
95	]
96	}
97	```
98
99	### CSV Format (Export)
100
101	```
102	id,token,direction,entry_date,entry_price,size_sol,strategy,exit_date,exit_price,pnl_sol,pnl_pct,outcome,lessons
103	T-20250310-001,SOL,long,2025-03-10T14:30:00Z,142.50,5.0,momentum-breakout,2025-03-10T16:45:00Z,146.20,0.648,2.60,win,"Patience paid off"
104	```
105
106	## Analytics from Journal Data
107
108	### Win Rate by Strategy
109
110	```python
111	from collections import Counter
112
113	def win_rate_by_strategy(trades: list[dict]) -> dict[str, float]:
114	"""Compute win rate grouped by strategy tag."""
115	strategy_outcomes: dict[str, list[str]] = {}
116	for t in trades:
117	strat = t["strategy"]
118	strategy_outcomes.setdefault(strat, []).append(t["outcome"])
119
120	return {
121	strat: outcomes.count("win") / len(outcomes)
122	for strat, outcomes in strategy_outcomes.items()
123	if len(outcomes) >= 5 # minimum sample size
124	}
125	```
126
127	### Performance by Time of Day
128
129	```python
130	from datetime import datetime
131
132	def pnl_by_hour(trades: list[dict]) -> dict[int, float]:
133	"""Aggregate P&L by entry hour (UTC)."""
134	hourly: dict[int, float] = {}
135	for t in trades:
136	hour = datetime.fromisoformat(t["entry_date"].rstrip("Z")).hour
137	hourly[hour] = hourly.get(hour, 0.0) + t.get("pnl_sol", 0.0)
138	return dict(sorted(hourly.items()))
139	```
140
141	### Profit Factor by Token Type
142
143	```python
144	def profit_factor(trades: list[dict], group_key: str = "token") -> dict[str, float]:
145	"""Compute profit factor (gross wins / gross losses)

People who liked this also viewed

6 picks

Type