aicoin-freqtrade

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_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:

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:

• 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

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

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

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