pandas-ta

Name: pandas-ta
Author: agiprolabs

295installs53forks

How to install pandas-ta?

agiprolabs/claude-trading-skills/pandas-ta

$npx -y skills add agiprolabs/claude-trading-skills --skill pandas-ta

Installs into the current project.

Summary

Technical analysis with 130+ indicators using pandas-ta for crypto market data

Files · 1

View on GitHub

SKILL.md

1	# pandas-ta — Technical Analysis for Crypto Markets
2
3	pandas-ta is a Python library that extends pandas DataFrames with 130+ technical analysis indicators accessible via `df.ta`. It covers trend, momentum, volatility, volume, and overlap indicator categories — all callable with a single method on any OHLCV DataFrame.
4
5	## Installation
6
7	```bash
8	uv pip install pandas-ta pandas httpx
9	```
10
11	## Quick Start
12
13	```python
14	import pandas as pd
15	import pandas_ta as ta
16
17	# Assume df is a DataFrame with columns: open, high, low, close, volume
18	# All lowercase column names required
19
20	# Single indicator
21	df["rsi"] = df.ta.rsi(length=14)
22	df["atr"] = df.ta.atr(length=14)
23
24	# Multiple indicators via strategy
25	df.ta.strategy(ta.Strategy(
26	name="Quick Check",
27	ta=[
28	{"kind": "rsi", "length": 14},
29	{"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
30	{"kind": "bbands", "length": 20, "std": 2.0},
31	]
32	))
33	```
34
35	## OHLCV DataFrame Format
36
37	pandas-ta expects a DataFrame with lowercase column names:
38
39	```python
40	import pandas as pd
41
42	df = pd.DataFrame({
43	"open": [...],
44	"high": [...],
45	"low": [...],
46	"close": [...],
47	"volume": [...]
48	}, index=pd.DatetimeIndex([...]))
49	```
50
51	Important: Set the index to a `DatetimeIndex` for time-aware indicators like VWAP. Column names must be lowercase (`close`, not `Close`).
52
53	### Handling Missing Data
54
55	```python
56	# Drop rows with NaN in OHLCV columns
57	df = df.dropna(subset=["open", "high", "low", "close", "volume"])
58
59	# Forward-fill small gaps (1-2 bars max)
60	df = df.ffill(limit=2)
61
62	# Verify no zero-volume bars for volume indicators
63	df = df[df["volume"] > 0]
64	```
65
66	## Core Indicator Categories
67
68	### Trend Indicators
69
70	Identify market direction and trend strength.
71
72	\| Indicator \| Call \| Key Signal \|
73	\|-----------\|------\|------------\|
74	\| SMA \| `df.ta.sma(length=20)` \| Price above = bullish \|
75	\| EMA \| `df.ta.ema(length=20)` \| Faster than SMA, less lag \|
76	\| SuperTrend \| `df.ta.supertrend(length=10, multiplier=3)` \| Direction column: 1=bull, -1=bear \|
77	\| Ichimoku \| `df.ta.ichimoku()` \| Returns tuple of (span, lines) DataFrames \|
78	\| VWMA \| `df.ta.vwma(length=20)` \| Volume-weighted price trend \|
79	\| HMA \| `df.ta.hma(length=20)` \| Minimal lag, smooth trend \|
80	\| ADX \| `df.ta.adx(length=14)` \| >25 = trending, <20 = ranging \|
81
82	### Momentum Indicators
83
84	Measure speed and magnitude of price changes.
85
86	\| Indicator \| Call \| Key Signal \|
87	\|-----------\|------\|------------\|
88	\| RSI \| `df.ta.rsi(length=14)` \| >70 overbought, <30 oversold \|
89	\| MACD \| `df.ta.macd(fast=12, slow=26, signal=9)` \| Histogram crossover = entry \|
90	\| Stochastic \| `df.ta.stoch(k=14, d=3, smooth_k=3)` \| >80 overbought, <20 oversold \|
91	\| CCI \| `df.ta.cci(length=20)` \| >100 overbought, <-100 oversold \|
92	\| Williams %R \| `df.ta.willr(length=14)` \| >-20 overbought, <-80 oversold \|
93	\| ROC \| `df.ta.roc(length=10)` \| Positive = upward momentum \|
94	\| MFI \| `df.ta.mfi(length=14)` \| Money flow version of RSI \|
95
96	### Volatility Indicators
97
98	Measure price dispersion and expected range.
99
100	\| Indicator \| Call \| Key Signal \|
101	\|-----------\|------\|------------\|
102	\| Bollinger Bands \| `df.ta.bbands(length=20, std=2)` \| Squeeze = breakout pending \|
103	\| ATR \| `df.ta.atr(length=14)` \| Position sizing, stop placement \|
104	\| Keltner Channels \| `df.ta.kc(length=20, scalar=1.5)` \| BB inside KC = squeeze \|
105	\| Donchian Channels \| `df.ta.donchian(lower_length=20, upper_length=20)` \| Breakout detection \|
106
107	### Volume Indicators
108
109	Confirm price moves with volume analysis.
110
111	\| Indicator \| Call \| Key Signal \|
112	\|-----------\|------\|------------\|
113	\| OBV \| `df.ta.obv()` \| Divergence from price = reversal \|
114	\| VWAP \| `df.ta.vwap()` \| Intraday fair value (needs DatetimeIndex) \|
115	\| CMF \| `df.ta.cmf(length=20)` \| >0 accumulation, <0 distribution \|
116	\| AD \| `df.ta.ad()` \| Accumulation/Distribution line \|
117
118	## Strategy Class
119
120	Run multiple indicators in a single call using `ta.Strategy`:
121
122	```python
123	import pandas_ta as ta
124
125	# Built-in "All" strategy runs every indicator
126	df.ta.strategy(ta.AllStrategy)
127
128	# Custom strategy
129	my_strategy = ta.Strategy(
130	name="Crypto Scalp",
131	description="Fast indicators for crypto scalping",
132	ta=[
133	{"kind": "ema", "length": 9},
134	{"kind": "ema", "length": 21},
135	{"kind": "rsi", "length": 7},
136	{"kind": "stoch", "k": 5, "d": 3, "smooth_k": 3},
137	{"kind": "atr", "length": 7},
138	{"kind": "bbands", "length": 10, "std": 2.0},
139	{"kind": "obv"},
140	]
141	)
142	df.ta.strategy(my_strategy)
143	```
144
145	### Named Strategy Patterns
146
147	```python
148	# Trend following
149	trend_strategy = ta.Strategy(
150	name="Trend",
151	ta=[
152	{"kind": "ema", "length": 20},
153	{"kind": "ema", "length": 50},
154	{"kind": "adx", "length": 14},
155	{"kind": "supertrend", "length": 10, "multiplier": 3},
156	{"kind": "atr", "length": 14},
157	]
158	)
159
160	# Mean reversion
161	reversion_strategy = ta.Strategy(
162	name="Mean Reversion",
163	ta=[
164	{"kind": "rsi

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pandas-ta

295installs53forks

View on GitHub

How to install pandas-ta?

agiprolabs/claude-trading-skills/pandas-ta

$npx -y skills add agiprolabs/claude-trading-skills --skill pandas-ta

Installs into the current project.

Summary

Technical analysis with 130+ indicators using pandas-ta for crypto market data

Files · 1

View on GitHub

SKILL.md

1	# pandas-ta — Technical Analysis for Crypto Markets
2
3	pandas-ta is a Python library that extends pandas DataFrames with 130+ technical analysis indicators accessible via `df.ta`. It covers trend, momentum, volatility, volume, and overlap indicator categories — all callable with a single method on any OHLCV DataFrame.
4
5	## Installation
6
7	```bash
8	uv pip install pandas-ta pandas httpx
9	```
10
11	## Quick Start
12
13	```python
14	import pandas as pd
15	import pandas_ta as ta
16
17	# Assume df is a DataFrame with columns: open, high, low, close, volume
18	# All lowercase column names required
19
20	# Single indicator
21	df["rsi"] = df.ta.rsi(length=14)
22	df["atr"] = df.ta.atr(length=14)
23
24	# Multiple indicators via strategy
25	df.ta.strategy(ta.Strategy(
26	name="Quick Check",
27	ta=[
28	{"kind": "rsi", "length": 14},
29	{"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
30	{"kind": "bbands", "length": 20, "std": 2.0},
31	]
32	))
33	```
34
35	## OHLCV DataFrame Format
36
37	pandas-ta expects a DataFrame with lowercase column names:
38
39	```python
40	import pandas as pd
41
42	df = pd.DataFrame({
43	"open": [...],
44	"high": [...],
45	"low": [...],
46	"close": [...],
47	"volume": [...]
48	}, index=pd.DatetimeIndex([...]))
49	```
50
51	Important: Set the index to a `DatetimeIndex` for time-aware indicators like VWAP. Column names must be lowercase (`close`, not `Close`).
52
53	### Handling Missing Data
54
55	```python
56	# Drop rows with NaN in OHLCV columns
57	df = df.dropna(subset=["open", "high", "low", "close", "volume"])
58
59	# Forward-fill small gaps (1-2 bars max)
60	df = df.ffill(limit=2)
61
62	# Verify no zero-volume bars for volume indicators
63	df = df[df["volume"] > 0]
64	```
65
66	## Core Indicator Categories
67
68	### Trend Indicators
69
70	Identify market direction and trend strength.
71
72	\| Indicator \| Call \| Key Signal \|
73	\|-----------\|------\|------------\|
74	\| SMA \| `df.ta.sma(length=20)` \| Price above = bullish \|
75	\| EMA \| `df.ta.ema(length=20)` \| Faster than SMA, less lag \|
76	\| SuperTrend \| `df.ta.supertrend(length=10, multiplier=3)` \| Direction column: 1=bull, -1=bear \|
77	\| Ichimoku \| `df.ta.ichimoku()` \| Returns tuple of (span, lines) DataFrames \|
78	\| VWMA \| `df.ta.vwma(length=20)` \| Volume-weighted price trend \|
79	\| HMA \| `df.ta.hma(length=20)` \| Minimal lag, smooth trend \|
80	\| ADX \| `df.ta.adx(length=14)` \| >25 = trending, <20 = ranging \|
81
82	### Momentum Indicators
83
84	Measure speed and magnitude of price changes.
85
86	\| Indicator \| Call \| Key Signal \|
87	\|-----------\|------\|------------\|
88	\| RSI \| `df.ta.rsi(length=14)` \| >70 overbought, <30 oversold \|
89	\| MACD \| `df.ta.macd(fast=12, slow=26, signal=9)` \| Histogram crossover = entry \|
90	\| Stochastic \| `df.ta.stoch(k=14, d=3, smooth_k=3)` \| >80 overbought, <20 oversold \|
91	\| CCI \| `df.ta.cci(length=20)` \| >100 overbought, <-100 oversold \|
92	\| Williams %R \| `df.ta.willr(length=14)` \| >-20 overbought, <-80 oversold \|
93	\| ROC \| `df.ta.roc(length=10)` \| Positive = upward momentum \|
94	\| MFI \| `df.ta.mfi(length=14)` \| Money flow version of RSI \|
95
96	### Volatility Indicators
97
98	Measure price dispersion and expected range.
99
100	\| Indicator \| Call \| Key Signal \|
101	\|-----------\|------\|------------\|
102	\| Bollinger Bands \| `df.ta.bbands(length=20, std=2)` \| Squeeze = breakout pending \|
103	\| ATR \| `df.ta.atr(length=14)` \| Position sizing, stop placement \|
104	\| Keltner Channels \| `df.ta.kc(length=20, scalar=1.5)` \| BB inside KC = squeeze \|
105	\| Donchian Channels \| `df.ta.donchian(lower_length=20, upper_length=20)` \| Breakout detection \|
106
107	### Volume Indicators
108
109	Confirm price moves with volume analysis.
110
111	\| Indicator \| Call \| Key Signal \|
112	\|-----------\|------\|------------\|
113	\| OBV \| `df.ta.obv()` \| Divergence from price = reversal \|
114	\| VWAP \| `df.ta.vwap()` \| Intraday fair value (needs DatetimeIndex) \|
115	\| CMF \| `df.ta.cmf(length=20)` \| >0 accumulation, <0 distribution \|
116	\| AD \| `df.ta.ad()` \| Accumulation/Distribution line \|
117
118	## Strategy Class
119
120	Run multiple indicators in a single call using `ta.Strategy`:
121
122	```python
123	import pandas_ta as ta
124
125	# Built-in "All" strategy runs every indicator
126	df.ta.strategy(ta.AllStrategy)
127
128	# Custom strategy
129	my_strategy = ta.Strategy(
130	name="Crypto Scalp",
131	description="Fast indicators for crypto scalping",
132	ta=[
133	{"kind": "ema", "length": 9},
134	{"kind": "ema", "length": 21},
135	{"kind": "rsi", "length": 7},
136	{"kind": "stoch", "k": 5, "d": 3, "smooth_k": 3},
137	{"kind": "atr", "length": 7},
138	{"kind": "bbands", "length": 10, "std": 2.0},
139	{"kind": "obv"},
140	]
141	)
142	df.ta.strategy(my_strategy)
143	```
144
145	### Named Strategy Patterns
146
147	```python
148	# Trend following
149	trend_strategy = ta.Strategy(
150	name="Trend",
151	ta=[
152	{"kind": "ema", "length": 20},
153	{"kind": "ema", "length": 50},
154	{"kind": "adx", "length": 14},
155	{"kind": "supertrend", "length": 10, "multiplier": 3},
156	{"kind": "atr", "length": 14},
157	]
158	)
159
160	# Mean reversion
161	reversion_strategy = ta.Strategy(
162	name="Mean Reversion",
163	ta=[
164	{"kind": "rsi

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