# ohlcv-processing

A skill that prepares OHLCV data: validates candles, fills gaps, flags anomalies and normalizes columns before analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 472 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/ohlcv-processing/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/ohlcv-processing

## What it does

The skill runs a full preparation pipeline for OHLCV data: column standardization, structural validation, gap handling, anomaly flagging and resampling. Everything expects one canonical DataFrame — a UTC `DatetimeIndex`, lowercase `open`, `high`, `low`, `close`, `volume` columns, sorted ascending with no duplicate timestamps.

The functions are plain Python. `standardize_columns` renames `vol`, `o`, `h`, `l`, `c` to standard names and raises if something is missing. `validate_ohlcv` localizes the index to UTC, sorts, drops duplicate timestamps and coerces types. `find_impossible_candles` catches bars where `high` sits below `low` or a price goes negative. `handle_gaps` rebuilds missing bars with `ffill` or `interpolate`, capped by `max_gap`, and `flag_anomalies` adds boolean columns for spikes, zero volume and impossible candles.

Dependencies are `pandas`, `numpy` and `httpx`, installed with `uv pip install pandas numpy httpx`. One thing to watch: `handle_gaps` fills at most 5 consecutive bars by default and leaves longer stretches as NaN. Fine for occasional API hiccups on minute data. Bigger holes need their own decision, or your indicators run on a broken series.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ohlcv-processing -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ohlcv-processing/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ohlcv-processing/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ohlcv-processing -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ohlcv-processing/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ohlcv-processing -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ohlcv-processing/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills ohlcv-processing
```

2. **Manually: put the files in place**
- File: `.github/skills/ohlcv-processing/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ohlcv-processing -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/ohlcv-processing
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ohlcv-processing/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill ohlcv-processing -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/ohlcv-processing/SKILL.md

## When to use it
- normalize columns coming from different data sources
- fill missing minute bars after an API outage
- catch impossible candles with negative prices
- flag price spikes and zero-volume bars
- resample candles into another timeframe

## Example prompts
- validate this OHLCV dataframe and list impossible candles
- fill the gaps in my minute data with forward fill
- add anomaly flags to the candle series before computing indicators

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ebf1dea2a43bd76f.

BEGIN THIRD-PARTY FILE ebf1dea2a43bd76f
````text
---
name: ohlcv-processing
description: Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging
---

# OHLCV Processing — Market Data Preparation

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.

**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.

## Quick Start

### 1. Install Dependencies

```bash
uv pip install pandas numpy httpx
```

### 2. Standard OHLCV DataFrame Format

All processing functions expect this canonical format:

```python
import pandas as pd

# Canonical OHLCV DataFrame
# - DatetimeIndex in UTC
# - Columns: open, high, low, close, volume (lowercase)
# - Sorted ascending by timestamp
# - No duplicate timestamps

df = pd.DataFrame({
    "open": [1.10, 1.12, 1.11],
    "high": [1.15, 1.14, 1.13],
    "low": [1.08, 1.10, 1.09],
    "close": [1.12, 1.11, 1.12],
    "volume": [50000, 48000, 52000],
}, index=pd.to_datetime([
    "2025-01-01 00:00:00",
    "2025-01-01 00:01:00",
    "2025-01-01 00:02:00",
], utc=True))
df.index.name = "timestamp"
```

### 3. Full Processing Pipeline

```python
import pandas as pd
import numpy as np

def process_ohlcv(df: pd.DataFrame) -> pd.DataFrame:
    """Run complete OHLCV processing pipeline."""
    df = standardize_columns(df)
    df = validate_ohlcv(df)
    df = handle_gaps(df, method="ffill")
    df = detect_and_flag_anomalies(df)
    return df
```

## Data Validation

### Column Checks

```python
REQUIRED_COLUMNS = {"open", "high", "low", "close", "volume"}

def standardize_columns(df: pd.DataFrame) -> pd.DataFrame:
    """Normalize column names to lowercase standard."""
    df.columns = df.columns.str.lower().str.strip()
    # Common renames
    rename_map = {"vol": "volume", "v": "volume", "o": "open",
                  "h": "high", "l": "low", "c": "close"}
    df = df.rename(columns=rename_map)
    missing = REQUIRED_COLUMNS - set(df.columns)
    if missing:
        raise ValueError(f"Missing columns: {missing}")
````
END THIRD-PARTY FILE ebf1dea2a43bd76f

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