用Python开发数字货币交易机器人（附源码）
众所周知，币圈一天，人间一年。我们进行数字货币交易时，在交易所 APP 或者网站 盯盘并手动下单非常耗时，当币价波动非常大的时候往往会错失良机。这时我们可以创建一个简单的 telegram 交易机器人，来帮助我们进行做空和做多交易。
该机器人可以实现以下功能：
- 做空交易 - 以指定的价格卖出持有货币并在价格下跌时回购
- 做多交易 - 指定的价格购买货币并在价格上涨时卖出
- 列出交易订单
- 显示可用余额
设置 Telegram 机器人
首先需要一个 Telegram 账号，如果没有的话请自己注册一个。然后与BotFather进行对话，通过输入/newbot来新建一个telegram机器人，根据指示一步步创建并记住你的token。
获取交易所的 API keys
查找你的交易所API文档，看看如何获取对订单和账户余额的访问权限和步骤，记住你的密码和API keys。本例中我们以bitfinex为例，Bitmex交易所是目前市面上交易量最大的比特币期货交易所，交易量和交易深度非常大。
安装依赖包
我们这边用的是Python 3.6版本，同时我们还需要利用CCXT框架获取Bitmex交易所数据，CCXT是一个JavaScript / Python / PHP 开发库，用于数字货币的交易，支持众多的比特币/以太币/山寨币交易市场和交易所API。
CCXT库用于连接数字货币交易所并在世界范围内进行交易和支付处理。使用 ccxt可以快速访问数字货币市场数据，可以用于存储、分析、可视化、指标开发、 量化交易、策略回溯测试、交易机器人程序以及相关的软件工程。
然后我们将使用python-telegram-bot与Telegram进行通讯，对聊天消息做出反应并进行交易。
只需要用下面方法安装以上两个依赖包：
pip install python-telegram-bot ccxt
我们需要交易机器人实现的基本类功能：
1、获取交易所概况，允许创建订单，列出订单详情并获取余额。这将是以 ccxt 实现的装饰器。
2、交易执行者，因为我们希望自动执行做空和做多交易。
3、即时响应的telegram 机器人。
编写机器人
项目结构如下：
main.py \config \core \model \util
我们将从一个简单的模型开始。因为多空交易两者有很多共同点，可以在\ model中创建一个基类TradeDetails：
import abc
class TradeDetails(metaclass=abc.ABCMeta):
def __init__(self, start_price: float, symbol: str, amount: float, currency: str = "USD"):
self.start_price = start_price
self.symbol = symbol.upper()
self.amount = amount
self.currency = currency
@property
def exchange_symbol(self):
return f"{self.symbol.upper()}/{self.currency}"
@property
@abc.abstractmethod
def exit_price(self):
pass
def __str__(self) -> str:
return f"order for {self.amount} {self.exchange_symbol} with enter price: {self.start_price:.5}, " \
f"exit_price: {self.exit_price:.5}" 具体的为：
- LongTrade
from fasttrade.model.trade import TradeDetails class LongTrade(TradeDetails): def __init__(self, start_price: float, symbol: str, amount: float, percent_change: float = 0.5, currency: str = "USD") -> None: super().__init__(start_price, symbol, amount, currency) self.end_price = start_price * (1 + percent_change / 100) @property def exit_price(self): return self.end_price def __str__(self) -> str: return "Long " + super().__str__()
- ShortTrade
from fasttrade.model.trade import TradeDetails class ShortTrade(TradeDetails): def __init__(self, start_price: float, symbol: str, amount: float, percent_change: float = 0.5, currency: str = "USD") -> None: super().__init__(start_price, symbol, amount, currency) self.end_price = start_price * (1 - percent_change / 100) @property def exit_price(self): return self.end_price def __str__(self) -> str: return "Short " + super().__str__()
接下来是获取交易所数据：
from ccxt import Exchange, OrderNotFound
class CryptoExchange:
def __init__(self, exchange: Exchange):
self.exchange = exchange
self.exchange.load_markets()
@property
def free_balance(self):
balance = self.exchange.fetch_free_balance()
# surprisingly there are balances with 0, so we need to filter these out
return {k: v for k, v in balance.items() if v > 0}
def fetch_open_orders(self, symbol: str = None):
return self.exchange.fetch_open_orders(symbolsymbol=symbol)
def fetch_order(self, order_id: int):
return self.exchange.fetch_order(order_id)
def cancel_order(self, order_id: int):
try:
self.exchange.cancel_order(order_id)
except OrderNotFound:
# treat as success
pass
def create_sell_order(self, symbol: str, amount: float, price: float):
return self.exchange.create_order(symbolsymbol=symbol, type="limit", side="sell", amountamount=amount, priceprice=price)
def create_buy_order(self, symbol: str, amount: float, price: float):
return self.exchange.create_order(symbolsymbol=symbol, type="limit", side="buy", amountamount=amount, priceprice=price) 然后，我们将执行交易程序。程序将接受交易所数据和超时情况以检查订单是否完成。当做空时，我们以设定的价格卖出，当价格下降到一定水平时回购。我们使用asyncio协程进行编码，以使等待不会阻塞：
import asyncio
import logging
from ccxt import ExchangeError
from model.longtrade import LongTrade
from model.shorttrade import ShortTrade
class TradeExecutor:
def __init__(self, exchange, check_timeout: int = 15):
self.check_timeout = check_timeout
self.exchange = exchange
async def execute_trade(self, trade):
if isinstance(trade, ShortTrade):
await self.execute_short_trade(trade)
elif isinstance(trade, LongTrade):
await self.execute_long_trade(trade)
async def execute_short_trade(self, trade: ShortTrade):
sell_price = trade.start_price
buy_price = trade.exit_price
symbol = trade.exchange_symbol
amount = trade.amount
order = self.exchange.create_sell_order(symbol, amount, sell_price)
logging.info(f'Opened sell order: {amount} of {symbol}. Target sell {sell_price}, buy price {buy_price}')
await self._wait_order_complete(order['id'])
# post buy order
order = self.exchange.create_buy_order(symbol, amount, buy_price)
await self._wait_order_complete(order['id'])
logging.info(f'Completed short trade: {amount} of {symbol}. Sold at {sell_price} and bought at {buy_price}')
async def execute_long_trade(self, trade: LongTrade):
buy_price = trade.start_price
sell_price = trade.exit_price
symbol = trade.exchange_symbol
amount = trade.amount
order = self.exchange.create_buy_order(symbol, amount, buy_price)
logging.info(f'Opened long trade: {amount} of {symbol}. Target buy {buy_price}, sell price {sell_price}')
await self._wait_order_complete(order.id)
# post sell order
order = self.exchange.create_sell_order(symbol, amount, sell_price)
await self._wait_order_complete(order.id)
logging.info(f'Completed long trade: {amount} of {symbol}. Bought at {buy_price} and sold at {sell_price}')
async def _wait_order_complete(self, order_id):
status = 'open'
while status is 'open':
await asyncio.sleep(self.check_timeout)
order = self.exchange.fetch_order(order_id)
status = order['status']
logging.info(f'Finished order {order_id} with {status} status')
# do not proceed further if we canceled order
if status == 'canceled':
raise ExchangeError('Trade has been canceled') ccxt使用REST API进行数据传输。它不如某些交易所支持的WebSockets快，但是对于这个简单的机器人来说，速度或许差别。
async def _wait_order_complete(self, order_id):
status = 'open'
order = None
while status is 'open':
await asyncio.sleep(self.check_timeout)
order = self.exchange.fetch_order(order_id)
status = order['status']
logging.info(f'Finished order {order_id} with {status} status')
# do not proceed further if we canceled order
if status == 'canceled':
raise ExchangeError('Trade has been canceled')
return order 接下来将创建Telegram机器人，这是最有难度的部分，我们将使其拥有以下指令：
1、列出/取消有效订单
2、显示可用余额
3、建立做多或做空交易
我们还需要对机器人做一些安全限制，使其仅对你的消息做出响应，而其他人则无法使用你的帐户进行交易。
主要是进行做多和做空交易的部分：
1、选择做空或者做多
2、输入数字货币品种
3、输入交易数量
4、所占百分比
5、每个价格
6、显示确认信息
7、显示最终交易信息
我们来创建telegrambot.py并添加以下常量：
SELECTION = "selection" SHORT_TRADE = "short_trade" LONG_TRADE = "long_trade" OPEN_ORDERS = "open_orders" FREE_BALANCE = "free_balance" CANCEL_ORD = "cancel_order" PROCESS_ORD_CANCEL = "process_ord_cancel" COIN_NAME = "coin_select" PERCENT_CHANGE = "percent_select" AMOUNT = "amount" PRICE = "price" PROCESS_TRADE = "process_trade" CONFIRM = "confirm" CANCEL = "cancel" END_CONVERSATION = ConversationHandler.END
我们可以通过扩展BaseFilter来实现对user_id的限制。这样机器人必须接受被允许用户的token、id才能执行操作。
class TelegramBot: class PrivateUserFiler(BaseFilter): def __init__(self, user_id): self.user_id = int(user_id) def filter(self, message): return message.from_user.id == self.user_id def __init__(self, token: str, allowed_user_id, trade_executor: TradeExecutor): self.updater = Updater(tokentoken=token) selfself.dispatcher = self.updater.dispatcher self.trade_executor = trade_executor selfself.exchange = self.trade_executor.exchange selfself.private_filter = self.PrivateUserFiler(allowed_user_id) self._prepare()
在_prepare（）函数中，我们将创建所有处理函数并将其附加到调度程序。我们开始与机器人聊天时希望显示的基本选项：
def _prepare(self):
# Create our handlers
def show_help(bot, update):
update.effective_message.reply_text('Type /trade to show options ')
def show_options(bot, update):
button_list = [
[InlineKeyboardButton("Short trade", callback_data=SHORT_TRADE),
InlineKeyboardButton("Long trade", callback_data=LONG_TRADE), ],
[InlineKeyboardButton("Open orders", callback_data=OPEN_ORDERS),
InlineKeyboardButton("Available balance", callback_data=FREE_BALANCE)],
]
update.message.reply_text("Trade options:", reply_markup=InlineKeyboardMarkup(button_list))
return TRADE_SELECT InlineKeyboardButton允许我们将文本选项显示为键盘。这比键入所有命令更为直观。callback_data允许在按下按钮时传递其他数据。show_options返回下一个继续进行对话的处理函数的名称。其他处理函数将使用类似的方法。然后我们执行用户选择的处理程序。在这里，我们主要从一个问题转到另一个问题：
def process_trade_selection(bot, update, user_data):
query = update.callback_query
selection = query.data
if selection == OPEN_ORDERS:
orders = self.exchange.fetch_open_orders()
if len(orders) == 0:
bot.edit_message_text(text="You don't have open orders",
chat_id=query.message.chat_id,
message_id=query.message.message_id)
return END_CONVERSATION
# show the option to cancel active orders
keyboard = [
[InlineKeyboardButton("Ok", callback_data=CONFIRM),
InlineKeyboardButton("Cancel order", callback_data=CANCEL)]
]
bot.edit_message_text(text=formatter.format_open_orders(orders),
chat_id=query.message.chat_id,
message_id=query.message.message_id,
reply_markup=InlineKeyboardMarkup(keyboard))
# attach opened orders, so that we can cancel by index
user_data[OPEN_ORDERS] = orders
return CANCEL_ORD
elif selection == FREE_BALANCE:
balance = self.exchange.free_balance
msg = "You don't have any available balance" if len(balance) == 0 \
else f"Your available balance:\n{formatter.format_balance(balance)}"
bot.edit_message_text(text=msg,
chat_id=query.message.chat_id,
message_id=query.message.message_id)
return END_CONVERSATION
user_data[TRADE_SELECT] = selection
bot.edit_message_text(text=f'Enter coin name for {selection}',
chat_id=query.message.chat_id,
message_id=query.message.message_id)
return COIN_NAME
def cancel_order(bot, update):
query = update.callback_query
if query.data == CANCEL:
query.message.reply_text('Enter order index to cancel: ')
return PROCESS_ORD_CANCEL
show_help(bot, update)
return END_CONVERSATION
def process_order_cancel(bot, update, user_data):
idx = int(update.message.text)
order = user_data[OPEN_ORDERS][idx]
self.exchange.cancel_order(order['id'])
update.message.reply_text(f'Canceled order: {formatter.format_order(order)}')
return END_CONVERSATION
def process_coin_name(bot, update, user_data):
user_data[COIN_NAME] = update.message.text.upper()
update.message.reply_text(f'What amount of {user_data[COIN_NAME]}')
return AMOUNT
def process_amount(bot, update, user_data):
user_data[AMOUNT] = float(update.message.text)
update.message.reply_text(f'What % change for {user_data[AMOUNT]} {user_data[COIN_NAME]}')
return PERCENT_CHANGE
def process_percent(bot, update, user_data):
user_data[PERCENT_CHANGE] = float(update.message.text)
update.message.reply_text(f'What price for 1 unit of {user_data[COIN_NAME]}')
return PRICE
def process_price(bot, update, user_data):
user_data[PRICE] = float(update.message.text)
keyboard = [
[InlineKeyboardButton("Confirm", callback_data=CONFIRM),
InlineKeyboardButton("Cancel", callback_data=CANCEL)]
]
update.message.reply_text(f"Confirm the trade: '{TelegramBot.build_trade(user_data)}'",
reply_markup=InlineKeyboardMarkup(keyboard))
return PROCESS_TRADE 最后，我们构建会话处理程序，设置错误处理程序，并将所有处理程序添加到调度程序中。
def process_trade(bot, update, user_data):
query = update.callback_query
if query.data == CONFIRM:
trade = TelegramBot.build_trade(user_data)
self._execute_trade(trade)
update.callback_query.message.reply_text(f'Scheduled: {trade}')
else:
show_help(bot, update)
return END_CONVERSATION
def handle_error(bot, update, error):
logging.warning('Update "%s" caused error "%s"', update, error)
update.message.reply_text(f'Unexpected error:\n{error}')
# configure our handlers
def build_conversation_handler():
entry_handler = CommandHandler('trade', filters=self.private_filter, callback=show_options)
conversation_handler = ConversationHandler(
entry_points=[entry_handler],
fallbacks=[entry_handler],
states={
TRADE_SELECT: [CallbackQueryHandler(process_trade_selection, pass_user_data=True)],
CANCEL_ORD: [CallbackQueryHandler(cancel_order)],
PROCESS_ORD_CANCEL: [MessageHandler(filters=Filters.text, callback=process_order_cancel, pass_user_data=True)],
COIN_NAME: [MessageHandler(filters=Filters.text, callback=process_coin_name, pass_user_data=True)],
AMOUNT: [MessageHandler(Filters.text, callback=process_amount, pass_user_data=True)],
PERCENT_CHANGE: [MessageHandler(Filters.text, callback=process_percent, pass_user_data=True)],
PRICE: [MessageHandler(Filters.text, callback=process_price, pass_user_data=True)],
PROCESS_TRADE: [CallbackQueryHandler(process_trade, pass_user_data=True)],
},
)
return conversation_handler
self.dispatcher.add_handler(CommandHandler('start', filters=self.private_filter, callback=show_help))
self.dispatcher.add_handler(build_conversation_handler())
self.dispatcher.add_error_handler(handle_error) 传递用户数据时允许我们向处理程序提供其他user_data参数。这样可以确保机器人从一个处理程序传递到另一个处理程序时，保持所有答复的对话状态。我们需要run_async装饰器在后台执行交易，而又不会阻止机器人对新消息进行响应：
def start_bot(self): self.updater.start_polling() @run_async def _execute_trade(self, trade): loop = asyncio.new_event_loop() task = loop.create_task(self.trade_executor.execute_trade(trade)) loop.run_until_complete(task) @staticmethod def build_trade(user_data): current_trade = user_data[TRADE_SELECT] price = user_data[PRICE] coin_name = user_data[COIN_NAME] amount = user_data[AMOUNT] percent_change = user_data[PERCENT_CHANGE] if current_trade == LONG_TRADE: return LongTrade(price, coin_name, amount, percent_change) elif current_trade == SHORT_TRADE: return ShortTrade(price, coin_name, amount, percent_change) else: raise NotImplementedError
这是用于订单和余额显示的格式化程序：
TITLES = ['idx', 'type', 'remaining', 'symbol', 'price']
SPACING = [4, 6, 8, 10, 8]
def format_open_orders(orders) -> str:
def join_line(ln):
return ' | '.join(str(item).center(SPACING[i]) for i, item in enumerate(ln))
title_line = join_line(TITLES)
lines = [title_line]
for idx, order in enumerate(orders):
line = [idx, order['side'], order['remaining'], order['symbol'], order['price']]
lines.append(join_line(line))
separator_line = '-' * len(title_line)
return f"\n{separator_line}\n".join(lines)
def format_order(order):
return f"{order['amount']} {order['symbol']} priced at {order['price']}"
def format_balance(balance) -> str:
coin_balance_as_list = list(f"{coin}: {val}" for coin, val in balance.items())
return "\n".join(coin_balance_as_list) 最后，我们创建main.py并将所有内容归结在一起：
import logging import os import ccxt from core.exchange import CryptoExchange from core.telegrambot import TelegramBot from core.tradeexcutor import TradeExecutor if __name__ == '__main__': logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO) c_dir = os.path.dirname(__file__) with open(os.path.join(c_dir, "config/secrets.txt")) as key_file: api_key, secret, telegram_tkn, user_id = key_file.read().splitlines() ccxtccxt_ex = ccxt.bitfinex() ccxt_ex.apiKey = api_key ccxt_ex.secret = secret exchange = CryptoExchange(ccxt_ex) trade_executor = TradeExecutor(exchange) telegram_bot = TelegramBot(telegram_tkn, user_id, trade_executor) telegram_bot.start_bot()
我们从secrets.txt文件中获取交易所密钥，telegram的token和用户ID，构造核心类并启动机器人。使用以下内容在config文件夹中创建secrets.txt：
# YOUR_API_KEY # YOUR_SECRET # YOUR_TELEGRAM_TOKEN # YOUR_TELEGRAM_USER_ID
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