ohlcv-processing

Name: ohlcv-processing
Author: agiprolabs

200installs53forks

How to install ohlcv-processing?

agiprolabs/claude-trading-skills/ohlcv-processing

$npx -y skills add agiprolabs/claude-trading-skills --skill ohlcv-processing

Installs into the current project.

Summary

Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging

Files · 1

View on GitHub

SKILL.md

1	# OHLCV Processing — Market Data Preparation
2
3	Clean, consistent OHLCV data is the foundation of every trading analysis. Garbage in, garbage out — a single anomalous candle can trigger false signals, corrupt indicator calculations, and produce misleading backtest results. This skill covers the full data preparation pipeline: validation, cleaning, resampling, normalization, and multi-source merging.
4
5	Why this matters: Crypto OHLCV data is messier than traditional markets. 24/7 trading means no official close, DEX aggregators disagree on prices, low-liquidity tokens produce impossible candles, and API outages create gaps. Every analysis workflow should start with this pipeline.
6
7	## Quick Start
8
9	### 1. Install Dependencies
10
11	```bash
12	uv pip install pandas numpy httpx
13	```
14
15	### 2. Standard OHLCV DataFrame Format
16
17	All processing functions expect this canonical format:
18
19	```python
20	import pandas as pd
21
22	# Canonical OHLCV DataFrame
23	# - DatetimeIndex in UTC
24	# - Columns: open, high, low, close, volume (lowercase)
25	# - Sorted ascending by timestamp
26	# - No duplicate timestamps
27
28	df = pd.DataFrame({
29	"open": [1.10, 1.12, 1.11],
30	"high": [1.15, 1.14, 1.13],
31	"low": [1.08, 1.10, 1.09],
32	"close": [1.12, 1.11, 1.12],
33	"volume": [50000, 48000, 52000],
34	}, index=pd.to_datetime([
35	"2025-01-01 00:00:00",
36	"2025-01-01 00:01:00",
37	"2025-01-01 00:02:00",
38	], utc=True))
39	df.index.name = "timestamp"
40	```
41
42	### 3. Full Processing Pipeline
43
44	```python
45	import pandas as pd
46	import numpy as np
47
48	def process_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
49	"""Run complete OHLCV processing pipeline."""
50	df = standardize_columns(df)
51	df = validate_ohlcv(df)
52	df = handle_gaps(df, method="ffill")
53	df = detect_and_flag_anomalies(df)
54	return df
55	```
56
57	## Data Validation
58
59	### Column Checks
60
61	```python
62	REQUIRED_COLUMNS = {"open", "high", "low", "close", "volume"}
63
64	def standardize_columns(df: pd.DataFrame) -> pd.DataFrame:
65	"""Normalize column names to lowercase standard."""
66	df.columns = df.columns.str.lower().str.strip()
67	# Common renames
68	rename_map = {"vol": "volume", "v": "volume", "o": "open",
69	"h": "high", "l": "low", "c": "close"}
70	df = df.rename(columns=rename_map)
71	missing = REQUIRED_COLUMNS - set(df.columns)
72	if missing:
73	raise ValueError(f"Missing columns: {missing}")
74	return df[["open", "high", "low", "close", "volume"]]
75	```
76
77	### Structural Validation
78
79	```python
80	def validate_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
81	"""Validate OHLCV structural integrity."""
82	# Ensure DatetimeIndex in UTC
83	if not isinstance(df.index, pd.DatetimeIndex):
84	df.index = pd.to_datetime(df.index, utc=True)
85	if df.index.tz is None:
86	df.index = df.index.tz_localize("UTC")
87
88	# Sort and deduplicate
89	df = df.sort_index()
90	dupes = df.index.duplicated(keep="last")
91	if dupes.any():
92	print(f"Warning: Removed {dupes.sum()} duplicate timestamps")
93	df = df[~dupes]
94
95	# Type enforcement
96	for col in ["open", "high", "low", "close", "volume"]:
97	df[col] = pd.to_numeric(df[col], errors="coerce")
98
99	return df
100	```
101
102	### Impossible Candle Detection
103
104	```python
105	def find_impossible_candles(df: pd.DataFrame) -> pd.DataFrame:
106	"""Find candles that violate OHLC constraints."""
107	issues = pd.DataFrame(index=df.index)
108	issues["high_lt_low"] = df["high"] < df["low"]
109	issues["high_lt_open"] = df["high"] < df["open"]
110	issues["high_lt_close"] = df["high"] < df["close"]
111	issues["low_gt_open"] = df["low"] > df["open"]
112	issues["low_gt_close"] = df["low"] > df["close"]
113	issues["negative_price"] = (df[["open", "high", "low", "close"]] < 0).any(axis=1)
114	issues["negative_volume"] = df["volume"] < 0
115	issues["any_issue"] = issues.any(axis=1)
116	return issues[issues["any_issue"]]
117	```
118
119	## Gap Handling
120
121	Crypto trades 24/7, but gaps still occur from API outages, low liquidity, or aggregator downtime.
122
123	### Detect Gaps
124
125	```python
126	def detect_gaps(df: pd.DataFrame, expected_freq: str = "1min") -> pd.Series:
127	"""Find missing timestamps based on expected frequency."""
128	full_index = pd.date_range(
129	start=df.index.min(), end=df.index.max(), freq=expected_freq, tz="UTC"
130	)
131	missing = full_index.difference(df.index)
132	return missing
133	```
134
135	### Fill Gaps
136
137	```python
138	def handle_gaps(
139	df: pd.DataFrame,
140	freq: str = "1min",
141	method: str = "ffill",
142	max_gap: int = 5,
143	) -> pd.DataFrame:
144	"""Fill gaps in OHLCV data.
145
146	Args:
147	df: OHLCV DataFrame with DatetimeIndex.
148	freq: Expected bar frequency.
149	method: 'ffill' (forward fill) or 'interpolate'.
150	max_gap: Maximum consecutive bars to fill. Larger gaps are left as NaN.
151	"""
152	full_index = pd.date_range(
153	start=df.index.min(), end=df.index.max(), freq=freq, tz="UTC"
154	)

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

ohlcv-processing

200installs53forks

View on GitHub

How to install ohlcv-processing?

agiprolabs/claude-trading-skills/ohlcv-processing

$npx -y skills add agiprolabs/claude-trading-skills --skill ohlcv-processing

Installs into the current project.

Summary

Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging

Files · 1

View on GitHub

SKILL.md

1	# OHLCV Processing — Market Data Preparation
2
3	Clean, consistent OHLCV data is the foundation of every trading analysis. Garbage in, garbage out — a single anomalous candle can trigger false signals, corrupt indicator calculations, and produce misleading backtest results. This skill covers the full data preparation pipeline: validation, cleaning, resampling, normalization, and multi-source merging.
4
5	Why this matters: Crypto OHLCV data is messier than traditional markets. 24/7 trading means no official close, DEX aggregators disagree on prices, low-liquidity tokens produce impossible candles, and API outages create gaps. Every analysis workflow should start with this pipeline.
6
7	## Quick Start
8
9	### 1. Install Dependencies
10
11	```bash
12	uv pip install pandas numpy httpx
13	```
14
15	### 2. Standard OHLCV DataFrame Format
16
17	All processing functions expect this canonical format:
18
19	```python
20	import pandas as pd
21
22	# Canonical OHLCV DataFrame
23	# - DatetimeIndex in UTC
24	# - Columns: open, high, low, close, volume (lowercase)
25	# - Sorted ascending by timestamp
26	# - No duplicate timestamps
27
28	df = pd.DataFrame({
29	"open": [1.10, 1.12, 1.11],
30	"high": [1.15, 1.14, 1.13],
31	"low": [1.08, 1.10, 1.09],
32	"close": [1.12, 1.11, 1.12],
33	"volume": [50000, 48000, 52000],
34	}, index=pd.to_datetime([
35	"2025-01-01 00:00:00",
36	"2025-01-01 00:01:00",
37	"2025-01-01 00:02:00",
38	], utc=True))
39	df.index.name = "timestamp"
40	```
41
42	### 3. Full Processing Pipeline
43
44	```python
45	import pandas as pd
46	import numpy as np
47
48	def process_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
49	"""Run complete OHLCV processing pipeline."""
50	df = standardize_columns(df)
51	df = validate_ohlcv(df)
52	df = handle_gaps(df, method="ffill")
53	df = detect_and_flag_anomalies(df)
54	return df
55	```
56
57	## Data Validation
58
59	### Column Checks
60
61	```python
62	REQUIRED_COLUMNS = {"open", "high", "low", "close", "volume"}
63
64	def standardize_columns(df: pd.DataFrame) -> pd.DataFrame:
65	"""Normalize column names to lowercase standard."""
66	df.columns = df.columns.str.lower().str.strip()
67	# Common renames
68	rename_map = {"vol": "volume", "v": "volume", "o": "open",
69	"h": "high", "l": "low", "c": "close"}
70	df = df.rename(columns=rename_map)
71	missing = REQUIRED_COLUMNS - set(df.columns)
72	if missing:
73	raise ValueError(f"Missing columns: {missing}")
74	return df[["open", "high", "low", "close", "volume"]]
75	```
76
77	### Structural Validation
78
79	```python
80	def validate_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
81	"""Validate OHLCV structural integrity."""
82	# Ensure DatetimeIndex in UTC
83	if not isinstance(df.index, pd.DatetimeIndex):
84	df.index = pd.to_datetime(df.index, utc=True)
85	if df.index.tz is None:
86	df.index = df.index.tz_localize("UTC")
87
88	# Sort and deduplicate
89	df = df.sort_index()
90	dupes = df.index.duplicated(keep="last")
91	if dupes.any():
92	print(f"Warning: Removed {dupes.sum()} duplicate timestamps")
93	df = df[~dupes]
94
95	# Type enforcement
96	for col in ["open", "high", "low", "close", "volume"]:
97	df[col] = pd.to_numeric(df[col], errors="coerce")
98
99	return df
100	```
101
102	### Impossible Candle Detection
103
104	```python
105	def find_impossible_candles(df: pd.DataFrame) -> pd.DataFrame:
106	"""Find candles that violate OHLC constraints."""
107	issues = pd.DataFrame(index=df.index)
108	issues["high_lt_low"] = df["high"] < df["low"]
109	issues["high_lt_open"] = df["high"] < df["open"]
110	issues["high_lt_close"] = df["high"] < df["close"]
111	issues["low_gt_open"] = df["low"] > df["open"]
112	issues["low_gt_close"] = df["low"] > df["close"]
113	issues["negative_price"] = (df[["open", "high", "low", "close"]] < 0).any(axis=1)
114	issues["negative_volume"] = df["volume"] < 0
115	issues["any_issue"] = issues.any(axis=1)
116	return issues[issues["any_issue"]]
117	```
118
119	## Gap Handling
120
121	Crypto trades 24/7, but gaps still occur from API outages, low liquidity, or aggregator downtime.
122
123	### Detect Gaps
124
125	```python
126	def detect_gaps(df: pd.DataFrame, expected_freq: str = "1min") -> pd.Series:
127	"""Find missing timestamps based on expected frequency."""
128	full_index = pd.date_range(
129	start=df.index.min(), end=df.index.max(), freq=expected_freq, tz="UTC"
130	)
131	missing = full_index.difference(df.index)
132	return missing
133	```
134
135	### Fill Gaps
136
137	```python
138	def handle_gaps(
139	df: pd.DataFrame,
140	freq: str = "1min",
141	method: str = "ffill",
142	max_gap: int = 5,
143	) -> pd.DataFrame:
144	"""Fill gaps in OHLCV data.
145
146	Args:
147	df: OHLCV DataFrame with DatetimeIndex.
148	freq: Expected bar frequency.
149	method: 'ffill' (forward fill) or 'interpolate'.
150	max_gap: Maximum consecutive bars to fill. Larger gaps are left as NaN.
151	"""
152	full_index = pd.date_range(
153	start=df.index.min(), end=df.index.max(), freq=freq, tz="UTC"
154	)

People who liked this also viewed

6 picks

Type