Aicoin Freqtrade
by @procaross
Use when user asks about Freqtrade — strategy creation, backtest, hyperopt, switching strategies / pairs / dry-run mode, querying live bot status / balance /...
clawhub install aicoin-freqtrade📖 About This Skill
name: aicoin-freqtrade description: "Use when user asks about writing trading strategies, backtesting, deploying Freqtrade bots, quantitative trading, or strategy optimization. Trigger words: 'write strategy', 'create strategy', 'backtest', 'deploy Freqtrade', 'deploy bot', 'quantitative', 'hyperopt', '写策略', '创建策略', '回测', '部署', '量化', '策略优化'. This skill provides: (1) create_strategy quick generator with 17 indicators, (2) AiCoin Python SDK (aicoin_data.py) for integrating real market data into custom strategies, (3) deploy/backtest/hyperopt tools. ALWAYS actively use AiCoin data (funding rate, L/S ratio, whale orders, etc.) in strategies when the user's API key supports it. For prices/charts use aicoin-market. For trading use aicoin-trading. For Hyperliquid use aicoin-hyperliquid." metadata: { "openclaw": { "primaryEnv": "AICOIN_ACCESS_KEY_ID", "requires": { "bins": ["node"] }, "homepage": "https://www.aicoin.com/opendata", "source": "https://github.com/aicoincom/coinos-skills", "license": "MIT" } }
> ⚠️ 运行脚本: 必须先 cd 到本 SKILL.md 所在目录再执行。示例: cd ~/.openclaw/workspace/skills/aicoin-freqtrade && node scripts/ft-deploy.mjs ...
AiCoin Freqtrade
Freqtrade strategy creation, backtesting, and deployment powered by AiCoin Open API.
Critical Rules
1. ALWAYS use ft-deploy.mjs backtest for backtesting. NEVER write custom backtest scripts. NEVER use simulated/fabricated data.
2. ALWAYS use ft-deploy.mjs deploy for deployment. NEVER use Docker. NEVER manually run freqtrade commands.
3. NEVER manually edit Freqtrade config files. Use ft-deploy.mjs actions.
4. NEVER manually run freqtrade trade, freqtrade status, freqtrade backtesting, source .venv/bin/activate, or pip install freqtrade. Always use ft-deploy.mjs or ft.mjs instead.
5. ACTIVELY use AiCoin data in strategies. Check what data the user's API key supports and integrate it. Don't only use basic indicators when richer data is available.
6. Freqtrade 不支持网格策略(grid)。 用户问网格时，说明限制并建议用趋势跟踪或区间策略替代。
Two Ways to Create Strategies
Option A: Quick Generator (for simple strategies)
create_strategy generates a ready-to-backtest strategy file with selected indicators and optional AiCoin data:
node scripts/ft-deploy.mjs create_strategy '{"name":"MACDStrategy","timeframe":"15m","indicators":["macd","rsi","atr"]}'
node scripts/ft-deploy.mjs create_strategy '{"name":"WhaleStrat","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate","ls_ratio"]}'
Available indicators: rsi, bb, ema, sma, macd, stochastic/kdj, atr, adx, cci, williams_r, vwap, ichimoku, volume_sma, obv
Option B: Write Custom Strategy Code (for complex/custom strategies)
When users need custom logic beyond what create_strategy offers, write a Python strategy file directly. Use the AiCoin Python SDK (aicoin_data.py, auto-installed at ~/.freqtrade/user_data/strategies/) to integrate real market data.
Strategy file location: ~/.freqtrade/user_data/strategies/YourStrategyName.py
#### AiCoin Python SDK Reference
from aicoin_data import AiCoinData, ccxt_to_aicoinac = AiCoinData(cache_ttl=300) # Auto-loads API keys from .env
Convert CCXT pair to AiCoin symbol
symbol = ccxt_to_aicoin("BTC/USDT:USDT", "binance") # → "btcswapusdt:binance"── Free tier (no key needed) ──
ac.coin_ticker("bitcoin") # Real-time price
ac.kline(symbol, period="3600") # K-line data (period in seconds)
ac.hot_coins("market") # Trending coins── 基础版 ($29/mo) ──
ac.funding_rate(symbol) # Funding rate history
ac.funding_rate(symbol, weighted=True) # Volume-weighted cross-exchange rate
ac.ls_ratio() # Aggregated long/short ratio── 标准版 ($79/mo) ──
ac.big_orders(symbol) # Whale/large orders
ac.agg_trades(symbol) # Aggregated large trades── 高级版 ($299/mo) ──
ac.liquidation_map(symbol, cycle="24h") # Liquidation heatmap
ac.liquidation_history(symbol) # Liquidation history── 专业版 ($699/mo) ──
ac.open_interest("BTC", interval="15m") # Aggregated open interest
ac.ai_analysis(["BTC"]) # AI-powered analysis
#### Complete Strategy Template (copy and customize)
# MyCustomStrategy - Description
Uses AiCoin data in live/dry_run mode
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
from pandas import DataFrame
import logging, timelogger = logging.getLogger(__name__)
class MyCustomStrategy(IStrategy):
INTERFACE_VERSION = 3
timeframe = '15m'
can_short = True
minimal_roi = {"0": 0.05, "60": 0.03, "120": 0.01}
stoploss = -0.05
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
# Hyperopt parameters
rsi_buy = IntParameter(20, 40, default=30, space='buy')
rsi_sell = IntParameter(60, 80, default=70, space='sell')
# AiCoin data cache
_ac_funding_rate = 0.0
_ac_ls_ratio = 0.5
_ac_whale_signal = 0.0
_ac_last_update = 0.0
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# === Technical Indicators (always available) ===
# RSI
delta = dataframe['close'].diff()
gain = delta.clip(lower=0).rolling(window=14).mean()
loss = (-delta.clip(upper=0)).rolling(window=14).mean()
rs = gain / loss
dataframe['rsi'] = 100 - (100 / (1 + rs))
# MACD
ema12 = dataframe['close'].ewm(span=12, adjust=False).mean()
ema26 = dataframe['close'].ewm(span=26, adjust=False).mean()
dataframe['macd'] = ema12 - ema26
dataframe['macd_signal'] = dataframe['macd'].ewm(span=9, adjust=False).mean()
# EMA
dataframe['ema_fast'] = dataframe['close'].ewm(span=8, adjust=False).mean()
dataframe['ema_slow'] = dataframe['close'].ewm(span=21, adjust=False).mean()
# === AiCoin Data (live/dry_run only) ===
dataframe['funding_rate'] = 0.0
dataframe['ls_ratio'] = 0.5
dataframe['whale_signal'] = 0.0
if self.dp and self.dp.runmode.value in ('live', 'dry_run'):
now = time.time()
if now - self._ac_last_update > 300: # Update every 5 min
self._update_aicoin_data(metadata)
self._ac_last_update = now
# Apply latest AiCoin data to current candle
dataframe.iloc[-1, dataframe.columns.get_loc('funding_rate')] = self._ac_funding_rate
dataframe.iloc[-1, dataframe.columns.get_loc('ls_ratio')] = self._ac_ls_ratio
dataframe.iloc[-1, dataframe.columns.get_loc('whale_signal')] = self._ac_whale_signal
return dataframe
def _update_aicoin_data(self, metadata: dict):
"""Fetch latest AiCoin data. Called every 5 min in live mode."""
try:
import sys, os
_sd = os.path.dirname(os.path.abspath(__file__))
if _sd not in sys.path:
sys.path.insert(0, _sd)
from aicoin_data import AiCoinData, ccxt_to_aicoin
ac = AiCoinData(cache_ttl=300)
pair = metadata.get('pair', 'BTC/USDT:USDT')
exchange = self.config.get('exchange', {}).get('name', 'binance')
symbol = ccxt_to_aicoin(pair, exchange)
# Funding rate (基础版)
try:
data = ac.funding_rate(symbol, weighted=True, limit='5')
items = data.get('data', [])
if isinstance(items, list) and items:
latest = items[0]
if isinstance(latest, dict) and 'close' in latest:
self._ac_funding_rate = float(latest['close']) * 100
except Exception as e:
logger.debug(f"AiCoin funding_rate unavailable: {e}")
# Long/short ratio (基础版)
try:
ls = ac.ls_ratio()
detail = ls.get('data', {}).get('detail', {})
if detail:
ratio = float(detail.get('last', 1.0))
self._ac_ls_ratio = max(0.0, min(1.0, ratio / (1.0 + ratio)))
except Exception as e:
logger.debug(f"AiCoin ls_ratio unavailable: {e}")
# Whale orders (标准版)
try:
orders = ac.big_orders(symbol)
if 'data' in orders and isinstance(orders['data'], list):
buy_vol = sum(float(o.get('amount', 0)) for o in orders['data']
if o.get('side', '').lower() in ('buy', 'bid', 'long'))
sell_vol = sum(float(o.get('amount', 0)) for o in orders['data']
if o.get('side', '').lower() in ('sell', 'ask', 'short'))
total = buy_vol + sell_vol
if total > 0:
self._ac_whale_signal = (buy_vol - sell_vol) / total
except Exception as e:
logger.debug(f"AiCoin big_orders unavailable: {e}")
except ImportError:
logger.warning("aicoin_data module not found. Run ft-deploy.mjs deploy to install.")
except Exception as e:
logger.warning(f"AiCoin data error: {e}")
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Long: RSI oversold + MACD bullish + AiCoin confirmations
dataframe.loc[
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['macd'] > dataframe['macd_signal']) &
(dataframe['ema_fast'] > dataframe['ema_slow']) &
(dataframe['ls_ratio'] <= 0.55) & # More shorts = contrarian long
(dataframe['whale_signal'] >= -0.3) & # Whales not heavily selling
(dataframe['volume'] > 0),
'enter_long'] = 1
# Short: RSI overbought + MACD bearish + AiCoin confirmations
dataframe.loc[
(dataframe['rsi'] > self.rsi_sell.value) &
(dataframe['macd'] < dataframe['macd_signal']) &
(dataframe['ema_fast'] < dataframe['ema_slow']) &
(dataframe['ls_ratio'] >= 0.45) & # More longs = contrarian short
(dataframe['whale_signal'] <= 0.3) & # Whales not heavily buying
(dataframe['volume'] > 0),
'enter_short'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(dataframe['rsi'] > 70), 'exit_long'] = 1
dataframe.loc[(dataframe['rsi'] < 30), 'exit_short'] = 1
return dataframe
AiCoin Data Integration Patterns
Use these patterns to integrate specific AiCoin data into entry/exit conditions:
| AiCoin Data | Signal Logic | Tier |
|-------------|-------------|------|
| funding_rate | Rate > 0.01% → market over-leveraged long → short signal; Rate < -0.01% → long signal | 基础版 |
| ls_ratio | Ratio < 0.45 (more shorts) → contrarian long; Ratio > 0.55 (more longs) → contrarian short | 基础版 |
| big_orders | (buy_vol - sell_vol) / total > 0.3 → whale buying → long; < -0.3 → short | 标准版 |
| open_interest | OI rising + price rising = healthy trend; OI rising + price falling = weak, likely reversal | 专业版 |
| liquidation_map | More short liquidations above → short squeeze likely → long; vice versa | 高级版 |
Key Rule: Backtest Behavior
AiCoin real-time data is NOT available for historical periods. In backtest mode:
Always explain this to the user when showing backtest results.
Quick Reference
| Task | Command |
|------|---------|
| Quick-generate strategy | node scripts/ft-deploy.mjs create_strategy '{"name":"MyStrat","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate"]}' |
| Backtest | node scripts/ft-deploy.mjs backtest '{"strategy":"MyStrat","timeframe":"1h","timerange":"20250101-20260301","pairs":["ETH/USDT:USDT"]}' |
| Deploy (dry-run) | node scripts/ft-deploy.mjs deploy '{"strategy":"MyStrat","pairs":["BTC/USDT:USDT"]}' |
| Deploy (live) | node scripts/ft-deploy.mjs deploy '{"strategy":"MyStrat","dry_run":false,"pairs":["BTC/USDT:USDT"]}' |
| Hyperopt | node scripts/ft-deploy.mjs hyperopt '{"strategy":"MyStrat","timeframe":"1h","timerange":"20250101-20260301","epochs":100}' |
| Strategy list | node scripts/ft-deploy.mjs strategy_list |
| Bot status | node scripts/ft-deploy.mjs status |
| Bot logs | node scripts/ft-deploy.mjs logs '{"lines":50}' |
Setup
Prerequisites: Python 3.11+ and git.
.env auto-loaded from (first found wins): cwd → ~/.openclaw/workspace/.env → ~/.openclaw/.env
Exchange keys (for live/dry-run):
BINANCE_API_KEY=xxx
BINANCE_API_SECRET=xxx
AiCoin API key (for AiCoin data in strategies):
AICOIN_ACCESS_KEY_ID=your-key-id
AICOIN_ACCESS_SECRET=your-secret
Scripts
ft-deploy.mjs — Deployment & Strategy
| Action | Params |
|--------|--------|
| check | None |
| deploy | {"strategy":"MACDKDJStrategy","dry_run":true,"pairs":["BTC/USDT:USDT"]} — strategy 必填，指定策略名 |
| backtest | {"strategy":"Name","timeframe":"1h","timerange":"20250101-20260301","pairs":["ETH/USDT:USDT"]} — pairs 可选，默认用 config 中的交易对 |
| hyperopt | {"strategy":"Name","timeframe":"1h","epochs":100} |
| create_strategy | {"name":"Name","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate"]} |
| strategy_list | None |
| backtest_results | None — lists recent backtest result files |
| start / stop / status / logs | None / {"lines":50} |
ft.mjs — Bot Control (requires running process)
ping, start, stop, balance, profit, trades_open, trades_history, force_enter, force_exit, daily, weekly, monthly, stats
ft-dev.mjs — Dev Tools (requires running process)
backtest_start, backtest_status, candles_live, candles_analyzed, strategy_list, strategy_get
Cross-Skill References
| Need | Use | |------|-----| | Prices, K-lines, market data | aicoin-market | | Exchange trading (buy/sell) | aicoin-trading | | Hyperliquid whale tracking | aicoin-hyperliquid |
Paid Feature Guide
When 304/403: Do NOT retry. Guide the user:
| Tier | Price | Data for Strategies |
|------|-------|---------------------|
| 免费版 | $0 | Pure technical indicators |
| 基础版 | $29/mo | + funding_rate, ls_ratio |
| 标准版 | $79/mo | + big_orders, agg_trades |
| 高级版 | $299/mo | + liquidation_map |
| 专业版 | $699/mo | + open_interest, ai_analysis |
Configure: AICOIN_ACCESS_KEY_ID + AICOIN_ACCESS_SECRET in .env
⚙️ Configuration
Prerequisites: Python 3.11+ and git.
.env auto-loaded from (first found wins): cwd → ~/.openclaw/workspace/.env → ~/.openclaw/.env
Exchange keys (for live/dry-run):
BINANCE_API_KEY=xxx
BINANCE_API_SECRET=xxx
AiCoin API key (for AiCoin data in strategies):
AICOIN_ACCESS_KEY_ID=your-key-id
AICOIN_ACCESS_SECRET=your-secret