Freqtrade 是一個用 Python 編寫的免費開源加密貨幣交易機器人。它旨在支持所有主要交易所并通過 Telegram 或 webUI 進行控制。功能包含回測、繪圖和資金管理工具以及通過機器學習的策略優化。
特性：

1. 基于 Python 3.8+ ：適用于任何操作系統 - Windows、macOS 和 Linux。
2. 持久性 ：持久性是通過 sqlite 實現的。
3. Dry-run ：不花錢運行機器人。
4. 回測 ：模擬買入/賣出策略。
5. 通過機器學習進行策略優化 ：使用機器學習通過真實的交易所數據優化買入/賣出策略參數。
6. 邊緣頭寸規模計算您的勝率、風險回報率、最佳止損位并在為每個特定市場建立頭寸之前調整頭寸規模。
7. 白名單加密貨幣 ：選擇你要交易的加密貨幣或使用動態白名單。
8. 黑名單加密貨幣 ：選擇你想要避免的加密貨幣。
9. 內置 WebUI ：內置 Web UI 來管理你的機器人。
10. 可通過 Telegram管理：使用 Telegram 管理機器人。
11. 以法定貨幣顯示盈虧 ：以法定貨幣顯示你的盈虧。
12. 表現狀態報告 ：提供你當前交易的表現狀態。

1.準備

開始之前，你要確保Python和pip已經成功安裝在電腦上，如果沒有，可以訪問這篇文章：超詳細Python安裝指南 進行安裝。

**(可選1) **如果你用Python的目的是數據分析，可以直接安裝Anaconda：Python數據分析與挖掘好幫手—Anaconda，它內置了Python和pip.

**(可選2) **此外，推薦大家用VSCode編輯器，它有許多的優點：Python 編程的最好搭檔—VSCode 詳細指南。

在Linux/MacOS下，三行命令就能完成安裝：

git clone -b develop https://github.com/freqtrade/freqtrade.git
cd freqtrade
./setup.sh --install

Windows環境下打開Cmd(開始—運行—CMD)，輸入命令安裝依賴：

git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
# 安裝ta-lib
pip install build_helpers/TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
pip install -r requirements.txt
pip install -e .
freqtrade

請注意，此處安裝ta-lib時項目方提供了python3.8/3.9/3.10，其他Python版本請自行搜索下載。

輸入freqtrade時，顯示以下信息說明安裝成功：

(freqtrade) D:CODEtraderfreqtrade >freqtrade
2022-02-17 19:40:50,174 - freqtrade - ERROR - Usage of Freqtrade requires a subcommand to be specified.
To have the bot executing trades in live/dry-run modes, depending on the value of the `dry_run` setting in the config, run Freqtrade as `freqtrade trade [options...]`.
To see the full list of options available, please use `freqtrade --help` or `freqtrade < command > --help`.

2.快速開始

下面教你如何開發一個簡單的交易策略。

一個策略文件往往包含這些東西：

• 指標
• 購買規則
• 賣出規則
• 建議最低投資回報率
• 強烈推薦止損

Freqtrade使用 Pandas 作為基礎數據結構，它底層的OHLCV都是以Dataframe的格式存儲的。

Dataframe數據流中每一行數據代表圖表上的一根K線，最新的K線始終是數據庫中最后一根。

dataframe.head()
                       date open high low close volume
0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292

Pandas 提供了計算指標的快速方法。為了從這種速度中受益，建議不要使用循環，而是使用矢量化方法。

矢量化操作在整個數據范圍內執行計算，因此，與遍歷每一行相比，在計算指標時要快得多。

dataframe.loc[(dataframe['rsi'] > 30), 'buy'] = 1

類似于上面這樣的賦值方法，會自動設置rsi大于30的數據的buy列的值為1。

買入規則

def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) - > DataFrame:
    """
    Based on TA indicators, populates the buy signal for the given dataframe
    :param dataframe: DataFrame populated with indicators
    :param metadata: Additional information, like the currently traded pair
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
            (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
            (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
            (dataframe['volume'] > 0) # Make sure Volume is not 0
        ),
        'buy'] = 1

    return dataframe

請注意，一定要不修改并返回"open", "high", "low", "close", "volume"列，這些是基礎行情數據，如果返回錯誤的數據將可能會導致一些奇怪數據的產生。

如上所示的方法中，符合條件的數據的buy值會被設為1代表買入，否則為0或nan值。

賣出規則

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) - > DataFrame:
    """
    Based on TA indicators, populates the sell signal for the given dataframe
    :param dataframe: DataFrame populated with indicators
    :param metadata: Additional information, like the currently traded pair
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
            (dataframe['tema'] > dataframe['bb_middleband']) & # Guard
            (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
            (dataframe['volume'] > 0) # Make sure Volume is not 0
        ),
        'sell'] = 1
    return dataframe

與買入類似，這里不贅述了。

最小投資回報率

在類中增加這個初始化變量，能控制投資回報率：

minimal_roi = {
    "40": 0.0,
    "30": 0.01,
    "20": 0.02,
    "0": 0.04
}

上述配置意味著：

• 只要達到 4% 的利潤就賣出
• 達到 2% 利潤時賣出（20 分鐘后生效）
• 達到 1% 利潤時賣出（30 分鐘后生效）
• 交易未虧損時賣出（40 分鐘后生效）

此處的計算包含費用。

要完全禁用 ROI，請將其設置為一個非常高的數字：

minimal_roi = {
    "0": 100
}

雖然從技術上講并沒有完全禁用，但一旦交易達到 10000% 利潤，它就會賣出。

止損

強烈建議設置止損，以保護資金免受不利的劇烈波動。

設置 10% 止損的示例：

stoploss = -0.10

一個完整代碼如下：

上滑查看更多代碼

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
from reimport A
import numpyas np# noqa
import pandasas pd# noqa
from pandasimport DataFrame

from freqtrade.strategyimport (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

# --------------------------------
# 你自己所需要的模塊放在這里
import talib.abstractas ta
import freqtrade.vendor.qtpylib.indicatorsas qtpylib


# This class is a sample. Feel free to customize it.
class SampleStrategy(IStrategy):
"""
    This is a sample strategy to inspire you.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/
    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy
    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION =2

# 設定最小投資回報
    minimal_roi = {
"60":0.01,
"30":0.02,
"0":0.04
    }

# 止損
    stoploss =-0.10

# 指標參數
    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)

# K線時間
    timeframe ='5m'

# 在新K線出現時執行
    process_only_new_candles =False

# These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal =True
    sell_profit_only =False
    ignore_roi_if_buy_signal =False

# 預準備K線數
    startup_candle_count: int =30

# 下單類型
    order_types = {
'buy':'limit',
'sell':'limit',
'stoploss':'market',
'stoploss_on_exchange':False
    }

# 訂單有效時間(gtc: 除非取消否則一直有效)
    order_time_in_force = {
'buy':'gtc',
'sell':'gtc'
    }

    plot_config = {
'main_plot': {
'tema': {},
'sar': {'color':'white'},
        },
'subplots': {
"MACD": {
'macd': {'color':'blue'},
'macdsignal': {'color':'orange'},
            },
"RSI": {
'rsi': {'color':'red'},
            }
        }
    }

def informative_pairs(self):
"""
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
return []

def populate_indicators(self, dataframe: DataFrame, metadata: dict) - > DataFrame:
"""
        Adds several different TA indicators to the given DataFrame
        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

# Momentum Indicators
# ------------------------------------

        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe)
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

# MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

# MFI
        dataframe['mfi'] = ta.MFI(dataframe)

# Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

# Parabolic SAR
        dataframe['sar'] = ta.SAR(dataframe)

# TEMA - Triple Exponential Moving Average
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)

        hilbert = ta.HT_SINE(dataframe)
        dataframe['htsine'] = hilbert['sine']
        dataframe['htleadsine'] = hilbert['leadsine']

return dataframe

def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) - > DataFrame:
"""
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
# Signal: RSI crosses above 30
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
                (dataframe['tema'] <= dataframe['bb_middleband']) &# Guard: tema below BB middle
                (dataframe['tema'] > dataframe['tema'].shift(1)) &# Guard: tema is raising
                (dataframe['volume'] >0)# Make sure Volume is not 0
            ),'buy'] =1

return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) - > DataFrame:
"""
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with sell column
        """
        dataframe.loc[
            (
# Signal: RSI crosses above 70
                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &
                (dataframe['tema'] > dataframe['bb_middleband']) &# Guard: tema above BB middle
                (dataframe['tema'] < dataframe['tema'].shift(1)) &# Guard: tema is falling
                (dataframe['volume'] >0)# Make sure Volume is not 0
            ),'sell'] =1
return dataframe

3.啟動機器人

啟動機器人前還需要設定配置，配置模板在 config/examples 下面。

比如幣安的配置，你還需要輸入key和secret：

"exchange": {
        "name": "binance",
        "key": "your_exchange_key",
        "secret": "your_exchange_secret",
        ......
  }
}

啟動機器人：

freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory -c path/far/far/away/config.json

--strategy-path 指定策略文件位置

-c 參數指定配置文件位置

比如我把策略放在了user_data/strategies下，配置放在了config_examples下，這么輸入命令啟動機器人即可：

freqtrade trade --strategy SampleStrategy --strategy-path user_data/strategies -c config_examples/config_binance.example.json

由于篇幅問題，本文只是介紹了freqtrade的冰山一角，在啟動機器人前，一定要進行回測并進行模擬交易。