⚠️ 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

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

aicoin_data.py

~/.freqtrade/user_data/strategies/

Strategy file location:

~/.freqtrade/user_data/strategies/YourStrategyName.py

AiCoin Python SDK Reference

from aicoin_data import AiCoinData, ccxt_to_aicoin

ac = 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, time

logger = 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:

funding_rate

ls_ratio

big_orders

(buy_vol - sell_vol) / total > 0.3

< -0.3

open_interest

liquidation_map

Key Rule: Backtest Behavior

AiCoin real-time data is NOT available for historical periods. In backtest mode:

• AiCoin columns use default values (funding_rate=0.0, ls_ratio=0.5, whale_signal=0.0)
• This means backtest results reflect technical indicators only
• Live/dry_run trading uses real AiCoin data, which should improve performance vs backtest

Always explain this to the user when showing backtest results.

Quick Reference

node scripts/ft-deploy.mjs create_strategy '{"name":"MyStrat","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate"]}'

node scripts/ft-deploy.mjs backtest '{"strategy":"MyStrat","timeframe":"1h","timerange":"20250101-20260301","pairs":["ETH/USDT:USDT"]}'

node scripts/ft-deploy.mjs deploy '{"strategy":"MyStrat","pairs":["BTC/USDT:USDT"]}'

node scripts/ft-deploy.mjs deploy '{"strategy":"MyStrat","dry_run":false,"pairs":["BTC/USDT:USDT"]}'

node scripts/ft-deploy.mjs hyperopt '{"strategy":"MyStrat","timeframe":"1h","timerange":"20250101-20260301","epochs":100}'

node scripts/ft-deploy.mjs strategy_list

node scripts/ft-deploy.mjs status

node scripts/ft-deploy.mjs logs '{"lines":50}'

Setup

Prerequisites: Python 3.11+ and git.

.env

~/.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

Get at https://www.aicoin.com/opendata

Scripts

ft-deploy.mjs — Deployment & Strategy

check

deploy

{"strategy":"MACDKDJStrategy","dry_run":true,"pairs":["BTC/USDT:USDT"]}

backtest

{"strategy":"Name","timeframe":"1h","timerange":"20250101-20260301","pairs":["ETH/USDT:USDT"]}

hyperopt

{"strategy":"Name","timeframe":"1h","epochs":100}

create_strategy

{"name":"Name","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate"]}

strategy_list

backtest_results

start

stop

status

logs

{"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

Paid Feature Guide

When 304/403: Do NOT retry. Guide the user:

funding_rate

ls_ratio

big_orders

agg_trades

liquidation_map

open_interest

ai_analysis

Configure:

AICOIN_ACCESS_KEY_ID

AICOIN_ACCESS_SECRET

.env