Official Bitget Skill · 本 Skill 由 Bitget 官方提供，市场数据来源可信，通过 Bitget Agent Hub 分发。 Data powered by Bitget market infrastructure · github.com/bitget-official/agent-hub

Technical Analysis Skill

23 crypto technical indicators across 6 categories (Trend, Volatility, Oscillator, Volume, Momentum, Support/Resistance). Outputs recent time-series data so the AI can observe trend evolution, not just a single point.

Vendor Neutrality

Present data as coming from "market data" — never name the underlying exchange, data feed, or library.

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Step 1: Check prerequisites

python -c "import pandas, numpy; print('OK')"

If missing:

pip install pandas numpy

Step 2: Determine indicator selection

Priority 1: User-defined
  The user explicitly specified indicator names, parameters, or combinations.
  -> Do NOT read scenarios.md or indicators.md
  -> Go directly to Step 3 with the user's config
  -> Example: "analyze BTC with RSI(21) + EMA(50) + ATR"

Priority 2: Scenario defaults
  The user described analysis intent but did not specify indicators.
  -> Read ONLY references/scenarios.md to pick the right scenario config
  -> Do NOT read indicators.md
  -> Example: "how's BTC looking technically?"

Priority 3: Indicator info query
  The user asks about an indicator rather than requesting a calculation.
  -> Read ONLY references/indicators.md
  -> No calculation needed, answer directly
  -> Example: "how does RSI work?"

Step 3: Run the calculation

The Python source is at src/ relative to this SKILL.md file.

For Claude Code the skill directory is ~/.claude/skills/technical-analysis. For Codex it is ~/.codex/skills/technical-analysis. For OpenClaw it is ~/.openclaw/skills/technical-analysis.

Template A: Fetch from Bitget API (default)

When the user mentions a trading pair but provides no local data:

import sys, os
sys.path.insert(0, os.path.expanduser('~/.claude/skills/technical-analysis/src'))
import json, urllib.request
import pandas as pd
from kline_indicator_utils import IndicatorManager

url = 'https://api.bitget.com/api/v2/spot/market/candles?symbol={SYMBOL}&granularity={GRANULARITY}&limit={LIMIT}'
raw = json.loads(urllib.request.urlopen(url).read())
df = pd.DataFrame(raw['data'], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'quoteVol', 'amount'])
for col in ['open', 'high', 'low', 'close', 'volume', 'amount']:
    df[col] = df[col].astype(float)

config = {CONFIG}

manager = IndicatorManager(show_indicators=False)
output = manager.calculate_and_export(config, df, tail={TAIL})
output["symbol"] = "{SYMBOL}"
output["granularity"] = "{GRANULARITY}"
print(json.dumps(output, indent=2))

Template B: Local data

When the user provides a local file path (CSV / Parquet / JSON):

import sys, os
sys.path.insert(0, os.path.expanduser('~/.claude/skills/technical-analysis/src'))
import json
import pandas as pd
from kline_indicator_utils import IndicatorManager

df = pd.read_csv('{FILE_PATH}')  # or pd.read_parquet / pd.read_json
df.columns = df.columns.str.lower()

for col in ['open', 'high', 'low', 'close', 'volume']:
    df[col] = df[col].astype(float)

config = {CONFIG}

manager = IndicatorManager(show_indicators=False)
output = manager.calculate_and_export(config, df, tail={TAIL})
output["source"] = "local: {FILE_PATH}"
print(json.dumps(output, indent=2))

Template C: Export full series to CSV

When the user asks for "full data", "export", or wants data for plotting/backtesting:

output = manager.calculate_and_export(config, df, tail={TAIL}, csv_path='{CSV_PATH}')

This saves OHLCV + all indicator columns to CSV for downstream use.

Template variables

Variable	Description	Example
SYMBOL	Trading pair	BTCUSDT, ETHUSDT
GRANULARITY	Kline interval	1h, 4h, 1d, 15min
LIMIT	Number of klines	200 (default)
CONFIG	Indicator config dict	See scenarios.md
TAIL	Number of recent bars in series output	50 (default)
CSV_PATH	File path for CSV export (optional)	./btc_indicators.csv

Choosing TAIL

Default is 50, which works well for most timeframes. Users can override:

• Increase to 100-200 for longer analysis windows
• Decrease to 20-30 to reduce output size

Bitget API interval mapping

User says	granularity value
1 minute	1min
5 minutes	5min
15 minutes	15min
30 minutes	30min
1 hour	1h
4 hours	4h
daily	1d
weekly	1w

Futures klines

Use a different API endpoint for futures:

url = 'https://api.bitget.com/api/v2/mix/market/candles?productType=USDT-FUTURES&symbol={SYMBOL}&granularity={GRANULARITY}&limit={LIMIT}'

Data requirement: DataFrame must contain at least open, high, low, close, volume columns (float). Optional: amount, timestamp. Column names are case-insensitive — normalize to lowercase.

Output format

The JSON output contains time-series arrays (not single points), so the AI can observe trend evolution and write natural-language interpretation.

For most indicators: series + context

{
  "MACD": {
    "series": {
      "DIF": [-120.5, -135.2, "...", -180.66],
      "DEA": [-85.1, -86.3, "...", -88.34],
      "HIST": [-70.8, -97.8, "...", -184.63]
    },
    "context": {
      "trend_streak": 7,
      "last_cross_up_bars_ago": 23,
      "last_cross_down_bars_ago": 9
    }
  }
}

For FIB: levels + context

FIB outputs static price levels for the lookback window — series is not meaningful:

{
  "FIB": {
    "levels": {
      "fib_high": 89200.5,
      "fib_236": 88750.2,
      "fib_382": 88430.1,
      "fib_500": 88100.0,
      "fib_618": 87770.3,
      "fib_786": 87340.5,
      "fib_low": 86950.0,
      "fib_strength": 35.2
    },
    "context": { "trend_streak": 12 }
  }
}

How to interpret and present

1. Read the series arrays — identify patterns: HIST narrowing/widening, RSI rising/falling, DIF/DEA converging/diverging, bandwidth expanding/contracting.
2. Write a conclusion-first summary (one line), then a table showing each indicator's recent behavior and current value.
3. For FIB, present as a price level table with the current price position marked.

Example output format:

## {SYMBOL} Technical Analysis ({GRANULARITY})

**Overall: {conclusion}**

| Dimension | Indicator | Recent Trend (last {TAIL} bars) | Current |
|-----------|-----------|--------------------------------|---------|
| Trend | SuperTrend | direction held for {N} bars | bullish |
| Momentum | MACD | HIST narrowing, DIF/DEA converging | DIF=-180.7 |
| Overbought/Oversold | RSI (14) | falling from 55 to 40 | 40.3 |

Agent behavior rules

1. Indicator selection priority: user-defined > scenario defaults > info query. When the user specifies indicators or parameters, follow them exactly — do not force a scenario template.

2. Precision rule: infer decimal places from the kline close price (e.g. BTC 63500.12 → 2 decimals, DOGE 0.0835 → 4 decimals). Round series values at that precision before presenting in tables; the raw JSON may have higher precision.

3. Conflicting indicators: present both sides objectively, do not favor either direction.

4. No trading advice: present data objectively. End with "The above is objective technical indicator data and does not constitute trading advice."

5. Label data source: always show timeframe, kline count, and data source (Bitget API / local file path).

Error Handling

When a tool or calculation fails, never name the underlying provider. Use neutral language:

Instead of…	Say…
"Bitget API returned 429"	"Market data is temporarily unavailable"
"pandas import error"	"Calculation environment is not ready — please install dependencies"
"Invalid symbol on Bitget"	"The trading pair could not be found in market data"

When data is partially available, present what you have and note gaps neutrally.

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Reference docs

• scenarios.md — 9 scenarios with indicator selection guide and output templates
• indicators.md — 23 indicator quick reference (params, outputs, signals)