class Kline:
def __init__(self):
config.loads('config.json')
self.symbol = "BTC-USDT-SWAP"
self.timeframe = "1m"
self.exchange = BINANCESWAP("", "", self.symbol)
self.indicators = INDICATORS(self.exchange, self.symbol,
self.timeframe)
def update(self):
table = self.exchange.get_kline(self.timeframe)
table.reverse()
try:
df = pd.DataFrame(
table,
columns='time open high low close volume currency_volume'.
split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except:
df = pd.DataFrame(
table, columns='time open high low close volume'.split())
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
boll = self.indicators.BOLL(20)
upperband = boll['upperband']
middleband = boll['middleband']
lowerband = boll['lowerband']
if not plots:
# first time we create the plots
global ax, ax2
plots.append(fplt.candlestick_ochl(candlesticks))
plots.append(fplt.volume_ocv(volumes, ax=ax2))
plots.append(fplt.plot(upperband, legend="UPPERBAND"))
plots.append(fplt.plot(middleband, legend="MIDDLEBAND"))
plots.append(fplt.plot(lowerband, legend="LOWERBAND"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(upperband)
plots[3].update_data(middleband)
plots[4].update_data(lowerband)
class Strategy:
"""布林强盗策略"""
def __init__(self, instrument_id, time_frame, bollinger_lengths,
filter_length, start_asset):
try:
# 策略启动时控制台输出提示信息
print("{} {} 布林强盗突破策略已启动！".format(get_localtime(),
instrument_id)) # 程序启动时打印提示信息
config.loads("config.json") # 载入配置文件
# 初始化
self.instrument_id = instrument_id # 合约ID
self.time_frame = time_frame # k线周期
self.exchange = OKEXFUTURES(config.access_key, config.secret_key,
config.passphrase,
self.instrument_id) # 交易所
self.market = MARKET(self.exchange, self.instrument_id,
self.time_frame) # 行情
self.position = POSITION(self.exchange, self.instrument_id,
self.time_frame) # 持仓
self.indicators = INDICATORS(self.exchange, self.instrument_id,
self.time_frame) # 指标
# 在第一次运行程序时，将初始资金、总盈亏等数据保存至数据库中
self.database = "回测" # 数据库，回测时必须为"回测"
self.datasheet = self.instrument_id.split(
"-")[0].lower() + "_" + time_frame # 数据表
if config.first_run == "true":
storage.mysql_save_strategy_run_info(self.database,
self.datasheet,
get_localtime(), "none",
0, 0, 0, 0, "none", 0, 0,
0, start_asset)
# 读取数据库中保存的总资金数据
self.total_asset = storage.read_mysql_datas(
0, self.database, self.datasheet, "总资金", ">")[-1][-1]
self.total_profit = storage.read_mysql_datas(
0, self.database, self.datasheet, "总资金", ">")[-1][-2] # 策略总盈亏
# 策略参数
self.contract_value = self.market.contract_value() # 合约面值
self.counter = 0 # 计数器
self.bollinger_lengths = bollinger_lengths # 布林通道参数
self.filter_length = filter_length # 过滤器参数
self.out_day = 50 # 自适应出场ma的初始值为50,开仓后赋值为布林通道参数的值
except:
logger.warning()
def begin_trade(self, kline=None):
try: # 异常处理
if self.indicators.CurrentBar(
kline=kline) < self.bollinger_lengths: # 如果k线数据不够长就返回
return
timestamp = ts_to_datetime_str(utctime_str_to_ts(
kline[-1]
[0])) if kline else get_localtime() # 非回测模式下时间戳就是当前本地时间
if self.indicators.BarUpdate(kline=kline):
self.counter = 0 # k线更新时还原计数器
if self.out_day > 10: # 计算MA的天数最小递减到10。如果达到10，则不再递减。
self.out_day -= 1 # 自适应出场ma的长度参数根据持仓周期递减，持有头寸的时间每多一天，计算MA的天数减1
deviation = float(
self.indicators.STDDEV(self.bollinger_lengths,
nbdev=2,
kline=kline)[-1]) # 标准差
middleband = float(
self.indicators.BOLL(self.bollinger_lengths,
kline=kline)['middleband'][-1]) # 布林通道中轨
upperband = float(middleband + deviation) # 布林通道上轨
lowerband = float(middleband - deviation) # 布林通道下轨
filter = float(
self.market.close(-1, kline=kline) - self.market.close(
(self.filter_length * -1) - 1,
kline=kline)) # 过滤器：当日收盘价减去30日前的收盘价
ma = float(self.indicators.MA(self.out_day,
kline=kline)[-1]) # 自适应移动出场平均线
# 策略主体
# 若k线数据足够长，且满足过滤条件，且当根k线最高价大于等于布林通道上轨，买入开多。
# 开仓处也设置计数器过滤，是为了防止没有启用交易助手的情况下挂单未成交，仓位为零时当根k线一直满足开仓条件，会重复挂单。
if self.indicators.CurrentBar(
kline=kline
) >= self.bollinger_lengths and filter > 0 and self.market.high(
-1, kline=kline) > upperband and self.counter < 1:
if self.position.amount() == 0: # 若当前无持仓
price = upperband # 开多价格为布林通道上轨的值
amount = round(self.total_asset / upperband /
self.contract_value) # 合约张数取整
info = self.exchange.buy(price,
amount) # 买入开多，并将返回的信息赋值给变量info
push(info) # 推送信息
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "买入开多",
price, amount, amount * price * self.contract_value,
price, "long", amount, 0, self.total_profit,
self.total_asset) # 将信息保存至数据库
self.counter += 1 # 此策略是在盘中开仓，而在回测时，每根bar只会运行一次，每根bar上的价格不分时间先后，故此处开仓后计数器加1，也就是当根k线不平仓
# 因为实盘时每个ticker进来策略就会运行一次。注意回测和实盘策略运行机制的不同。
self.out_day = self.bollinger_lengths # 开仓后赋值
# 开空
if self.indicators.CurrentBar(
kline=kline
) >= self.bollinger_lengths and filter < 0 and self.market.low(
-1, kline=kline) < lowerband and self.counter < 1:
if self.position.amount() == 0:
price = lowerband
amount = round(self.total_asset / upperband /
self.contract_value)
info = self.exchange.sellshort(price, amount)
push(info)
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "卖出开空",
price, amount, amount * price * self.contract_value,
price, "short", amount, 0, self.total_profit,
self.total_asset)
self.counter += 1
self.out_day = self.bollinger_lengths # 开仓后赋值
# 如果当前持多，且当根k线最低价小于等于中轨值，触发保护性止损，就平多止损
# 因为回测是一根k线上运行整个策略一次，所以要实现当根k线开仓后当根k线不平仓，需要将self.counter < 1的条件加在平仓的地方
if self.position.direction() == "long" and self.market.low(
-1, kline=kline) < middleband and self.counter < 1:
profit = self.position.coverlong_profit(
last=middleband) # 此处计算平多利润时，传入最新价last为中轨值，也就是触发止损价格的那个值。
self.total_profit += profit # 计算经过本次盈亏后的总利润
self.total_asset += profit # 计算经过本次盈亏后的总资金
price = middleband # 平多价格为中轨值
amount = self.position.amount() # 平仓数量为当前持仓数量
info = self.exchange.sell(price, amount)
push(info)
self.counter += 1
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "卖出止损", price,
amount, price * amount * self.contract_value, 0, "none", 0,
profit, self.total_profit, self.total_asset)
if self.position.direction() == "short" and self.market.high(
-1, kline=kline) > middleband and self.counter < 1:
profit = self.position.covershort_profit(last=middleband)
self.total_profit += profit
self.total_asset += profit
price = middleband
amount = self.position.amount()
info = self.exchange.buytocover(price, amount)
push(info)
self.counter += 1
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "买入止损", price,
amount, amount * price * self.contract_value, 0, "none", 0,
profit, self.total_profit, self.total_asset)
# 平多
if self.position.direction(
) == "long" and upperband > ma > self.market.low(
-1, kline=kline) and self.counter < 1:
profit = self.position.coverlong_profit(last=ma)
self.total_profit += profit
self.total_asset += profit
price = ma # 平仓价格为自适应出场均线的值
amount = self.position.amount()
info = self.exchange.sell(price, amount)
push(info)
self.counter += 1
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "卖出平多", price,
amount, price * amount * self.contract_value, 0, "none", 0,
profit, self.total_profit, self.total_asset)
# 平空
if self.position.direction(
) == "short" and lowerband < ma < self.market.high(
-1, kline=kline) and self.counter < 1:
profit = self.position.covershort_profit(last=ma)
self.total_profit += profit
self.total_asset += profit
price = ma
amount = self.position.amount()
info = self.exchange.buytocover(price, amount)
push(info)
self.counter += 1
storage.mysql_save_strategy_run_info(
self.database, self.datasheet, timestamp, "买入平空", price,
amount, amount * price * self.contract_value, 0, "none", 0,
profit, self.total_profit, self.total_asset)
except:
logger.error()
class SIGNALIZE:
"""实盘时根据从交易所获取的k线数据绘制k线图、成交量图及指标"""
def __init__(self, platform, symbol, time_frame):
self.__platform = platform
self.__symbol = symbol
self.__time_frame = time_frame
self.__market = MARKET(self.__platform, self.__symbol,
self.__time_frame)
# pull some data
self.__indicators = INDICATORS(self.__platform, self.__symbol,
self.__time_frame)
self.__kline = platform.get_kline(self.__time_frame)
self.__kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
self.__df = pd.DataFrame(self.__kline,
columns=[
'time', 'open', 'high', 'low',
'close', 'volume', 'currency_volume'
])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
self.__df = pd.DataFrame(
self.__kline,
columns=['time', 'open', 'high', 'low', 'close', 'volume'])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
self.__ax, self.__ax2, self.__ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = self.__df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=self.__ax)
# overlay volume on the plot
volumes = self.__df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=self.__ax2)
fplt.add_legend("VOLUME", self.__ax2) # 增加"VOLUME"图例
"""
plot indicators
"""
def show(self):
"""最后必须调用此函数以显示图像"""
fplt.show()
def plot_last(self, color=None):
"""在图上画出最新成交价这根横线，便于观察"""
last = self.__market.last()
array = np.empty(len(self.__kline))
array.fill(last)
color = color if color is not None else "#CD7F32" # 默认设置为红色
fplt.plot(self.__df['time'],
array,
color=color,
ax=self.__ax,
legend="LAST {}".format(last))
def plot_array(self, array, ax, legend, color=None):
"""
绘制任意的数组成线性
:param array: 传入一个数组
:param ax: 加载在第几行的图上
:param legend: 图例名称
:param color: 颜色
:return:
"""
if ax == 1:
ax = self.__ax
elif ax == 2:
ax = self.__ax2
elif ax == 3:
ax = self.__ax3
color = color if color is not None else "#FF0000" # 默认设置为红色
fplt.plot(self.__df['time'], array, color=color, ax=ax, legend=legend)
def plot_atr(self, length, color=None):
"""
在图上画出ATR
:param length: ATR指标参数
:param color: 线的颜色
:return:
"""
color = color if color is not None else "#FF0000" # 默认设置为红色
fplt.plot(self.__df['time'],
self.__indicators.ATR(length),
color=color,
ax=self.__ax3,
legend='ATR({})'.format(length))
def plot_boll(self, length, color1=None, color2=None, color3=None):
"""
在图上画出布林通道的上轨、中轨、下轨
:param length: BOLL指标参数
:param upperband_color: 上轨颜色
:param middleband_color: 中轨颜色
:param lowerband_color: 下轨颜色
:return:
"""
color1 = color1 if color1 is not None else "#FF0000" # 默认设置为红色
color2 = color2 if color2 is not None else "#00FF00" # 默认设置为绿色
color3 = color3 if color3 is not None else "#0000FF" # 默认设置为蓝色
upperband_array = self.__indicators.BOLL(length)['upperband']
middleband_array = self.__indicators.BOLL(length)["middleband"]
lowerband_array = self.__indicators.BOLL(length)["lowerband"]
fplt.plot(self.__df['time'],
upperband_array,
color=color1,
ax=self.__ax,
legend='BOLL({})-UPPERBAND'.format(length))
fplt.plot(self.__df['time'],
middleband_array,
color=color2,
ax=self.__ax,
legend='BOLL({})-MIDDLEBAND'.format(length))
fplt.plot(self.__df['time'],
lowerband_array,
color=color3,
ax=self.__ax,
legend='BOLL({})-LOWERBAND'.format(length))
# 副图上也加载
fplt.plot(self.__df['time'],
upperband_array,
color=color1,
ax=self.__ax3,
legend='BOLL({})-UPPERBAND'.format(length))
fplt.plot(self.__df['time'],
middleband_array,
color=color2,
ax=self.__ax3,
legend='BOLL({})-MIDDLEBAND'.format(length))
fplt.plot(self.__df['time'],
lowerband_array,
color=color3,
ax=self.__ax3,
legend='BOLL({})-LOWERBAND'.format(length))
def plot_highest(self, length, color=None):
"""
在图上画出最高价
:param length: HIGHEST指标参数
:param color: 线的颜色
:return:
"""
color = color if color is not None else "#FF0000" # 默认设置红黑色
fplt.plot(self.__df['time'],
self.__indicators.HIGHEST(length),
color=color,
ax=self.__ax,
legend='HIGHEST({})'.format(length))
# 副图也加载
fplt.plot(self.__df['time'],
self.__indicators.HIGHEST(length),
color=color,
ax=self.__ax3,
legend='HIGHEST({})'.format(length))
def plot_ma(self, length, color=None):
"""
在图上画出移动平均线
:param length: 简单移动平均线参数
:param color: 线的颜色
:return:
"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 主图与副图加载指标
fplt.plot(self.__df['time'],
self.__indicators.MA(length),
color=color,
ax=self.__ax,
legend='MA({})'.format(length))
fplt.plot(self.__df['time'],
self.__indicators.MA(length),
color=color,
ax=self.__ax3,
legend='MA({})'.format(length))
def plot_macd(self,
fastperiod,
slowperiod,
signalperiod,
color1=None,
color2=None,
color3=None):
"""
在图上画出MACD指标
:param fastperiod:
:param slowperiod:
:param signalperiod:
:param color1:
:param color2:
:param color3:
:return:
"""
color1 = color1 if color1 is not None else "#FF0000" # 默认设置为红色
color2 = color2 if color2 is not None else "#00FF00" # 默认设置为绿色
color3 = color3 if color3 is not None else "#0000FF" # 默认设置为蓝色
dif = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)['DIF']
dea = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)["DEA"]
macd = self.__indicators.MACD(fastperiod, slowperiod,
signalperiod)["MACD"]
fplt.plot(self.__df['time'],
dif,
color=color1,
ax=self.__ax3,
legend='MACD({}, {}, {})-DIF'.format(fastperiod, slowperiod,
signalperiod))
fplt.plot(self.__df['time'],
dea,
color=color2,
ax=self.__ax3,
legend='MACD({}, {}, {})-DEA'.format(fastperiod, slowperiod,
signalperiod))
fplt.plot(self.__df['time'],
macd,
color=color3,
ax=self.__ax3,
legend='MACD({}, {}, {})-MACD'.format(
fastperiod, slowperiod, signalperiod))
def plot_ema(self, length, color=None):
"""
在图上画出EMA指标
:param length:
:param color:
:return:
"""
color = color if color is not None else "#FF0000" # 默认设置为红色
fplt.plot(self.__df['time'],
self.__indicators.EMA(length),
color=color,
ax=self.__ax,
legend='EMA({})'.format(length))
# 副图也加载
fplt.plot(self.__df['time'],
self.__indicators.EMA(length),
color=color,
ax=self.__ax3,
legend='EMA({})'.format(length))
def plot_kama(self, length, color=None):
"""在图上画出KAMA指标"""
color = color if color is not None else "#FF0000" # 默认设置为红色
fplt.plot(self.__df['time'],
self.__indicators.KAMA(length),
color=color,
ax=self.__ax,
legend='KAMA({})'.format(length))
# 副图也加载
fplt.plot(self.__df['time'],
self.__indicators.KAMA(length),
color=color,
ax=self.__ax3,
legend='KAMA({})'.format(length))
def plot_kdj(self,
fastk_period,
slowk_period,
slowd_period,
color1=None,
color2=None):
"""
在图上画出KDJ指标
:param fastk_period:
:param slowk_period:
:param slowd_period:
:param color1:
:param color2:
:param color3:
:return:
"""
color1 = color1 if color1 is not None else "#FF0000" # 默认设置为红色
color2 = color2 if color2 is not None else "#00FF00" # 默认设置为绿色
k = self.__indicators.KDJ(fastk_period, slowk_period,
slowd_period)['k']
d = self.__indicators.KDJ(fastk_period, slowk_period,
slowd_period)["d"]
# 仅副图加载
fplt.plot(self.__df['time'],
k,
color=color1,
ax=self.__ax3,
legend='KDJ({}, {}, {})-K'.format(fastk_period, slowk_period,
slowd_period))
fplt.plot(self.__df['time'],
d,
color=color2,
ax=self.__ax3,
legend='KDJ({}, {}, {})-D'.format(fastk_period, slowk_period,
slowd_period))
def plot_lowest(self, length, color=None):
"""LOWEST"""
color = color if color is not None else "#FF0000" # 默认设置红黑色
fplt.plot(self.__df['time'],
self.__indicators.LOWEST(length),
color=color,
ax=self.__ax,
legend='LOWEST({})'.format(length))
# 副图也加载
fplt.plot(self.__df['time'],
self.__indicators.LOWEST(length),
color=color,
ax=self.__ax3,
legend='LOWEST({})'.format(length))
def plot_obv(self, color=None):
"""OBV"""
color = color if color is not None else "#FF0000" # 默认设置红黑色
# 仅副图加载
fplt.plot(self.__df['time'],
self.__indicators.OBV(),
color=color,
ax=self.__ax3,
legend='OBV')
def plot_rsi(self, length, color=None):
"""RSI"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 仅副图加载
fplt.plot(self.__df['time'],
self.__indicators.RSI(length),
color=color,
ax=self.__ax3,
legend='RSI({})'.format(length))
def plot_roc(self, length, color=None):
"""ROC"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 仅副图加载
fplt.plot(self.__df['time'],
self.__indicators.ROC(length),
color=color,
ax=self.__ax3,
legend='ROC({})'.format(length))
def plot_stochrsi(self,
timeperiod,
fastk_period,
fastd_period,
color1=None,
color2=None):
"""STOCHRSI"""
color1 = color1 if color1 is not None else "#FF0000" # 默认设置为红色
color2 = color2 if color2 is not None else "#00FF00" # 默认设置为绿色
stochrsi = self.__indicators.STOCHRSI(timeperiod, fastk_period,
fastd_period)['stochrsi']
fastk = self.__indicators.STOCHRSI(timeperiod, fastk_period,
fastd_period)["fastk"]
# 仅副图加载
fplt.plot(self.__df['time'],
stochrsi,
color=color1,
ax=self.__ax3,
legend='STOCHRSI({}, {}, {})-STOCHRSI'.format(
timeperiod, fastk_period, fastd_period))
fplt.plot(self.__df['time'],
fastk,
color=color2,
ax=self.__ax3,
legend='STOCHRSI({}, {}, {})-FASTK'.format(
timeperiod, fastk_period, fastd_period))
def plot_sar(self, color=None):
"""
在图上画出SAR
:param length: SAR指标参数
:param color: 线的颜色
:return:
"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 主副图均加载
fplt.plot(self.__df['time'],
self.__indicators.SAR(),
color=color,
ax=self.__ax,
legend='SAR')
fplt.plot(self.__df['time'],
self.__indicators.SAR(),
color=color,
ax=self.__ax3,
legend='SAR')
def plot_stddev(self, length, color=None):
"""STDDEV"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 仅副图加载
fplt.plot(self.__df['time'],
self.__indicators.STDDEV(length),
color=color,
ax=self.__ax3,
legend='STDDEV({})'.format(length))
def plot_trix(self, length, color=None):
"""STDDEV"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 仅副图加载
fplt.plot(self.__df['time'],
self.__indicators.TRIX(length),
color=color,
ax=self.__ax3,
legend='TRIX({})'.format(length))
def plot_volume(self, color=None):
"""VOLUME"""
color = color if color is not None else "#FF0000" # 默认设置为红色
# 仅副图均加载
fplt.plot(self.__df['time'],
self.__indicators.VOLUME(),
color=color,
ax=self.__ax3,
legend='VOLUME')