portfolio-analytics

Name: portfolio-analytics
Author: agiprolabs

216installs53forks

How to install portfolio-analytics?

agiprolabs/claude-trading-skills/portfolio-analytics

$npx -y skills add agiprolabs/claude-trading-skills --skill portfolio-analytics

Installs into the current project.

Summary

Portfolio-level performance measurement including return metrics, risk metrics, risk-adjusted ratios, rolling analysis, and HTML reports

Files · 1

View on GitHub

SKILL.md

1	# Portfolio Analytics
2
3	Compute portfolio-level performance metrics from equity curves and trade logs. Covers return metrics, risk metrics, risk-adjusted ratios, drawdown analysis, rolling windows, benchmark comparison, trade-level statistics, and automated HTML report generation via quantstats.
4
5	## When to Use This Skill
6
7	- After backtesting a strategy (e.g., from `vectorbt` or `strategy-framework`)
8	- Comparing multiple strategies or parameter sets side-by-side
9	- Generating investor-ready performance reports
10	- Evaluating live trading performance against benchmarks
11	- Assessing risk-adjusted returns for portfolio allocation decisions
12
13	## Prerequisites
14
15	```bash
16	uv pip install pandas numpy quantstats
17	```
18
19	## Input Format
20
21	All analytics start from an equity curve — a time-indexed Series of portfolio values:
22
23	```python
24	import pandas as pd
25	import numpy as np
26
27	# From a backtest
28	equity = pd.Series(
29	[10000, 10150, 10080, 10320, 10510, 10440, 10680],
30	index=pd.date_range("2025-01-01", periods=7, freq="D"),
31	name="strategy_equity"
32	)
33
34	# Convert to returns
35	returns = equity.pct_change().dropna()
36	```
37
38	## Return Metrics
39
40	### Total Return
41
42	```python
43	total_return = (equity.iloc[-1] / equity.iloc[0]) - 1
44	```
45
46	### CAGR (Compound Annual Growth Rate)
47
48	```python
49	days = (equity.index[-1] - equity.index[0]).days
50	cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1
51	```
52
53	### Daily Mean Return
54
55	```python
56	daily_mean = returns.mean()
57	annualized_mean = daily_mean * 252 # trading days
58	```
59
60	### Cumulative Returns
61
62	```python
63	cumulative = (1 + returns).cumprod() - 1
64	```
65
66	## Risk Metrics
67
68	### Annualized Volatility
69
70	```python
71	daily_vol = returns.std()
72	annual_vol = daily_vol * np.sqrt(252)
73	```
74
75	### Value at Risk (VaR)
76
77	Historical VaR at a given confidence level:
78
79	```python
80	def historical_var(returns: pd.Series, confidence: float = 0.95) -> float:
81	"""Compute historical VaR.
82
83	Args:
84	returns: Daily return series.
85	confidence: Confidence level (e.g., 0.95 for 95%).
86
87	Returns:
88	VaR as a positive number representing potential loss.
89	"""
90	return -np.percentile(returns, (1 - confidence) * 100)
91	```
92
93	### Conditional VaR (CVaR / Expected Shortfall)
94
95	```python
96	def historical_cvar(returns: pd.Series, confidence: float = 0.95) -> float:
97	"""Mean of returns below the VaR threshold."""
98	var = historical_var(returns, confidence)
99	return -returns[returns <= -var].mean()
100	```
101
102	### Maximum Drawdown
103
104	```python
105	def max_drawdown(equity: pd.Series) -> float:
106	"""Maximum peak-to-trough decline."""
107	peak = equity.cummax()
108	drawdown = (equity - peak) / peak
109	return drawdown.min() # negative number
110
111	def drawdown_series(equity: pd.Series) -> pd.Series:
112	"""Full drawdown time series."""
113	peak = equity.cummax()
114	return (equity - peak) / peak
115	```
116
117	### Time Underwater
118
119	```python
120	def time_underwater(equity: pd.Series) -> int:
121	"""Longest consecutive period below previous peak (in days)."""
122	dd = drawdown_series(equity)
123	is_underwater = dd < 0
124	groups = (~is_underwater).cumsum()
125	underwater_periods = is_underwater.groupby(groups).sum()
126	return int(underwater_periods.max()) if len(underwater_periods) > 0 else 0
127	```
128
129	## Risk-Adjusted Ratios
130
131	### Sharpe Ratio
132
133	```python
134	def sharpe_ratio(
135	returns: pd.Series,
136	rf: float = 0.0,
137	periods_per_year: int = 252
138	) -> float:
139	"""Annualized Sharpe ratio.
140
141	Args:
142	returns: Period returns.
143	rf: Risk-free rate per period.
144	periods_per_year: Annualization factor.
145
146	Returns:
147	Annualized Sharpe ratio.
148	"""
149	excess = returns - rf
150	if excess.std() == 0:
151	return 0.0
152	return (excess.mean() / excess.std()) * np.sqrt(periods_per_year)
153	```
154
155	### Sortino Ratio
156
157	```python
158	def sortino_ratio(
159	returns: pd.Series,
160	rf: float = 0.0,
161	periods_per_year: int = 252
162	) -> float:
163	"""Annualized Sortino ratio (penalizes only downside vol)."""
164	excess = returns - rf
165	downside = excess[excess < 0]
166	if len(downside) == 0 or downside.std() == 0:
167	return float("inf") if excess.mean() > 0 else 0.0
168	return (excess.mean() / downside.std()) * np.sqrt(periods_per_year)
169	```
170
171	### Calmar Ratio
172
173	```python
174	def calmar_ratio(equity: pd.Series, periods_per_year: int = 252) -> float:
175	"""CAGR divided by max drawdown (absolute value)."""
176	returns = equity.pct_change().dropna()
177	days = (equity.index[-1] - equity.index[0]).days
178	cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1
179	mdd = abs(max_drawdown(equity))
180	if mdd == 0:
181	return float("inf") if cagr > 0 else 0.0
182	return cagr / mdd
183	```
184
185	### Omega Ratio
186
187	```python
188	def omega_ratio(
189	returns: pd.Series,
190	threshold: float = 0.0
191	) -> float:
192	"""Ratio of probability-weighted gains to losses."""
193	excess =

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

portfolio-analytics

216installs53forks

View on GitHub

How to install portfolio-analytics?

agiprolabs/claude-trading-skills/portfolio-analytics

$npx -y skills add agiprolabs/claude-trading-skills --skill portfolio-analytics

Installs into the current project.

Summary

Portfolio-level performance measurement including return metrics, risk metrics, risk-adjusted ratios, rolling analysis, and HTML reports

Files · 1

View on GitHub

SKILL.md

1	# Portfolio Analytics
2
3	Compute portfolio-level performance metrics from equity curves and trade logs. Covers return metrics, risk metrics, risk-adjusted ratios, drawdown analysis, rolling windows, benchmark comparison, trade-level statistics, and automated HTML report generation via quantstats.
4
5	## When to Use This Skill
6
7	- After backtesting a strategy (e.g., from `vectorbt` or `strategy-framework`)
8	- Comparing multiple strategies or parameter sets side-by-side
9	- Generating investor-ready performance reports
10	- Evaluating live trading performance against benchmarks
11	- Assessing risk-adjusted returns for portfolio allocation decisions
12
13	## Prerequisites
14
15	```bash
16	uv pip install pandas numpy quantstats
17	```
18
19	## Input Format
20
21	All analytics start from an equity curve — a time-indexed Series of portfolio values:
22
23	```python
24	import pandas as pd
25	import numpy as np
26
27	# From a backtest
28	equity = pd.Series(
29	[10000, 10150, 10080, 10320, 10510, 10440, 10680],
30	index=pd.date_range("2025-01-01", periods=7, freq="D"),
31	name="strategy_equity"
32	)
33
34	# Convert to returns
35	returns = equity.pct_change().dropna()
36	```
37
38	## Return Metrics
39
40	### Total Return
41
42	```python
43	total_return = (equity.iloc[-1] / equity.iloc[0]) - 1
44	```
45
46	### CAGR (Compound Annual Growth Rate)
47
48	```python
49	days = (equity.index[-1] - equity.index[0]).days
50	cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1
51	```
52
53	### Daily Mean Return
54
55	```python
56	daily_mean = returns.mean()
57	annualized_mean = daily_mean * 252 # trading days
58	```
59
60	### Cumulative Returns
61
62	```python
63	cumulative = (1 + returns).cumprod() - 1
64	```
65
66	## Risk Metrics
67
68	### Annualized Volatility
69
70	```python
71	daily_vol = returns.std()
72	annual_vol = daily_vol * np.sqrt(252)
73	```
74
75	### Value at Risk (VaR)
76
77	Historical VaR at a given confidence level:
78
79	```python
80	def historical_var(returns: pd.Series, confidence: float = 0.95) -> float:
81	"""Compute historical VaR.
82
83	Args:
84	returns: Daily return series.
85	confidence: Confidence level (e.g., 0.95 for 95%).
86
87	Returns:
88	VaR as a positive number representing potential loss.
89	"""
90	return -np.percentile(returns, (1 - confidence) * 100)
91	```
92
93	### Conditional VaR (CVaR / Expected Shortfall)
94
95	```python
96	def historical_cvar(returns: pd.Series, confidence: float = 0.95) -> float:
97	"""Mean of returns below the VaR threshold."""
98	var = historical_var(returns, confidence)
99	return -returns[returns <= -var].mean()
100	```
101
102	### Maximum Drawdown
103
104	```python
105	def max_drawdown(equity: pd.Series) -> float:
106	"""Maximum peak-to-trough decline."""
107	peak = equity.cummax()
108	drawdown = (equity - peak) / peak
109	return drawdown.min() # negative number
110
111	def drawdown_series(equity: pd.Series) -> pd.Series:
112	"""Full drawdown time series."""
113	peak = equity.cummax()
114	return (equity - peak) / peak
115	```
116
117	### Time Underwater
118
119	```python
120	def time_underwater(equity: pd.Series) -> int:
121	"""Longest consecutive period below previous peak (in days)."""
122	dd = drawdown_series(equity)
123	is_underwater = dd < 0
124	groups = (~is_underwater).cumsum()
125	underwater_periods = is_underwater.groupby(groups).sum()
126	return int(underwater_periods.max()) if len(underwater_periods) > 0 else 0
127	```
128
129	## Risk-Adjusted Ratios
130
131	### Sharpe Ratio
132
133	```python
134	def sharpe_ratio(
135	returns: pd.Series,
136	rf: float = 0.0,
137	periods_per_year: int = 252
138	) -> float:
139	"""Annualized Sharpe ratio.
140
141	Args:
142	returns: Period returns.
143	rf: Risk-free rate per period.
144	periods_per_year: Annualization factor.
145
146	Returns:
147	Annualized Sharpe ratio.
148	"""
149	excess = returns - rf
150	if excess.std() == 0:
151	return 0.0
152	return (excess.mean() / excess.std()) * np.sqrt(periods_per_year)
153	```
154
155	### Sortino Ratio
156
157	```python
158	def sortino_ratio(
159	returns: pd.Series,
160	rf: float = 0.0,
161	periods_per_year: int = 252
162	) -> float:
163	"""Annualized Sortino ratio (penalizes only downside vol)."""
164	excess = returns - rf
165	downside = excess[excess < 0]
166	if len(downside) == 0 or downside.std() == 0:
167	return float("inf") if excess.mean() > 0 else 0.0
168	return (excess.mean() / downside.std()) * np.sqrt(periods_per_year)
169	```
170
171	### Calmar Ratio
172
173	```python
174	def calmar_ratio(equity: pd.Series, periods_per_year: int = 252) -> float:
175	"""CAGR divided by max drawdown (absolute value)."""
176	returns = equity.pct_change().dropna()
177	days = (equity.index[-1] - equity.index[0]).days
178	cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1
179	mdd = abs(max_drawdown(equity))
180	if mdd == 0:
181	return float("inf") if cagr > 0 else 0.0
182	return cagr / mdd
183	```
184
185	### Omega Ratio
186
187	```python
188	def omega_ratio(
189	returns: pd.Series,
190	threshold: float = 0.0
191	) -> float:
192	"""Ratio of probability-weighted gains to losses."""
193	excess =

People who liked this also viewed

6 picks

Type