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数字货币行情获取和指标计算演示
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‎.gitignore

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__pycache__/

‎MyTT.py

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# MyTT 麦语言-通达信-同花顺指标实现 https://github.com/mpquant/MyTT
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import numpy as np; import pandas as pd
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def RD(N,D=3): return np.round(N,D) #四舍五入取3位小数
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def RET(S,N=1): return np.array(S)[-N] #返回序列倒数第N个值,默认返回最后一个
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def ABS(S): return np.abs(S) #返回N的绝对值
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def MAX(S1,S2): return np.maximum(S1,S2) #序列max
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def MIN(S1,S2): return np.minimum(S1,S2) #序列min
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def MA(S,N): #求序列的N日平均值，返回序列
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return pd.Series(S).rolling(N).mean().values
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def REF(S, N=1): # 对序列整体下移动N,返回序列(shift后会产生NAN)
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return pd.Series(S).shift(N).values
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def DIFF(S, N=1): #前一个值减后一个值,前面会产生nan
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return pd.Series(S).diff(N) #np.diff(S)直接删除nan，会少一行
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def STD(S,N): #求序列的N日标准差，返回序列
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return pd.Series(S).rolling(N).std(ddof=0).values
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def IF(S_BOOL,S_TRUE,S_FALSE): # res=S_TRUE if S_BOOL==True else S_FALSE
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return np.where(S_BOOL, S_TRUE, S_FALSE)
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def SUM(S, N): #对序列求N天累计和，返回序列
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return pd.Series(S).rolling(N).sum().values
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def HHV(S,N): # HHV(C, 5) # 最近5天收盘最高价
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return pd.Series(S).rolling(N).max().values
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def LLV(S,N): # LLV(C, 5) # 最近5天收盘最低价
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return pd.Series(S).rolling(N).min().values
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def EMA(S,N): #为了精度 S>4*N EMA至少需要120周期
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return pd.Series(S).ewm(span=N, adjust=False).mean().values
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def SMA(S, N, M=1): #中国式的SMA,至少需要120周期才精确
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K = pd.Series(S).rolling(N).mean() #先求出平均值
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for i in range(N+1, len(S)): K[i] = (M * S[i] + (N -M) * K[i-1]) / N # 因为要取K[i-1]，所以 range(N+1, len(S))
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return K
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def AVEDEV(S,N): # 平均绝对偏差 (序列与其平均值的绝对差的平均值)
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avedev=pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean())
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return avedev.values
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#--------------------------------------------------------------------
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def COUNT(S_BOOL, N): # COUNT(C>O, N): 最近N天满足S_BOO的天数 True的天数
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return SUM(S_BOOL,N)
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def EVERY(S_BOOL, N): # EVERY(C>O, 5) 最近N天是否都是True
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R=SUM(S_BOOL, N)
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return IF(R==N, True, False)
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def EXIST(S_BOOL, N=5): # EXIST(CLOSE>3010, N=5) n日内是否存在一天大于3000点
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R=SUM(S_BOOL,N)
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return IF(R>0, True ,False)
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def CROSS(S1,S2): # 判断穿越 CROSS(MA(C,5),MA(C,10))
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CROSS_BOOL=IF(S1>S2, True ,False) #;print(CROSS_BOOL)
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return COUNT(CROSS_BOOL>0,2)==1 # 上穿：昨天0 今天1 下穿：昨天1 今天0
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#-----------------------------------下面是具体指标-------------------------------------
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def MACD(CLOSE,SHORT=12,LONG=26,M=9): # EMA的关系，S取120日，和雪球小数点2位相同
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DIF = EMA(CLOSE,SHORT)-EMA(CLOSE,LONG);
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DEA = EMA(DIF,M); MACD=(DIF-DEA)*2
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return RD(DIF),RD(DEA),RD(MACD)
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def KDJ(CLOSE,HIGH,LOW, N=9,M1=3,M2=3):
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RSV = (CLOSE - LLV(LOW, N)) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
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K = EMA(RSV, (M1*2-1)); D = EMA(K,(M2*2-1)); J=K*3-D*2
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return K, D, J
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def RSI(CLOSE, N=24):
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DIF = CLOSE-REF(CLOSE,1)
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return RD(SMA(MAX(DIF,0), N) / SMA(ABS(DIF), N) * 100)
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def WR(CLOSE, N=10, N1=6): #W&R 威廉指标
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WR = (HHV(HIGH, N) - CLOSE) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
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WR1 = (HHV(HIGH, N1) - CLOSE) / (HHV(HIGH, N1) - LLV(LOW, N1)) * 100
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return RD(WR), RD(WR1)
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def BIAS(CLOSE,L1=6, L2=12, L3=24): # BIAS乖离率
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BIAS1 = (CLOSE - MA(CLOSE, L1)) / MA(CLOSE, L1) * 100
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BIAS2 = (CLOSE - MA(CLOSE, L2)) / MA(CLOSE, L2) * 100
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BIAS3 = (CLOSE - MA(CLOSE, L3)) / MA(CLOSE, L3) * 100
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return RD(BIAS1), RD(BIAS2), RD(BIAS3)
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def BOLL(CLOSE,N=20, P=2): #BOLL布林带
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MID = MA(CLOSE, N);
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UPPER = MID + STD(CLOSE, N) * P
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LOWER = MID - STD(CLOSE, N) * P
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return RD(UPPER), RD(MID), RD(LOWER)
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def PSY(CLOSE,N=12, M=6):
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PSY=COUNT(CLOSE>REF(CLOSE,1),N)/N*100
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PSYMA=MA(PSY,M)
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return RD(PSY),RD(PSYMA)
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def CCI(CLOSE,HIGH,LOW,N=14):
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TP=(HIGH+LOW+CLOSE)/3
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return (TP-MA(TP,N))/(0.015*AVEDEV(TP,N))
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def ATR(CLOSE,HIGH,LOW, N=20): #真实波动N日平均值
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TR = MAX(MAX((HIGH - LOW), ABS(REF(CLOSE, 1) - HIGH)), ABS(REF(CLOSE, 1) - LOW))
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return MA(TR, N)
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‎example1.py

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#数字货币行情获取和指标计算演示
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from hb_hq_api import *
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from MyTT import *
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df=get_price('btc.usdt',count=120,frequency='1d'); #日线数据获取 1d:1天 4h:4小时 60m: 60分钟 15m:15分钟
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CLOSE=df.close.values; OPEN=df.open.values; HIGH=df.high.values; LOW=df.low.values #基础数据定义
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MA5=MA(CLOSE,5)
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MA10=MA(CLOSE,10)
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CROSS_TODAY=RET(CROSS(MA5,MA10))
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print(f'BTC5日均线{ MA5[-1]} BTC10日均线 {MA10[-1]}' )
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print('今天5日线是否上穿10日线',CROSS_TODAY)
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print('最近5天收盘价全都大于5日线吗？',EVERY(CLOSE>MA10,5) )
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DIF,DEA,MACD=MACD(CLOSE)
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print('MACD值',DIF,DEA,MACD)

‎hb_hq_api.py

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#火币行情功能函数
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import json,requests,urllib3,datetime; import pandas as pd; import numpy as np ; urllib3.disable_warnings()
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huobi_domain='api.huobi.de.com' #API地址 'api.huobi.pro' api.huobi.be没有被墙 api.huobi.de.com
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class Context: #根类
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def __init__(self):
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self.current_dt=datetime.datetime.now()
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# #1d:1天 4h:4小时 60m: 60分钟 15m:15分钟
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def get_price(code, end_date=None, count=1,frequency='1d', fields=['close']):
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code=code.replace('.','')
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frequency=frequency.replace('d','day').replace('m','min').replace('h','hour')
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url = f'https://{huobi_domain}/market/history/kline?period={frequency}&size={count}&symbol={code}'
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res = requests.get(url,verify=False).text
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df=pd.DataFrame(json.loads(res)['data']); df=df.iloc[::-1] #排序翻转
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df["time"] = pd.to_datetime(df["id"]+8*3600, unit='s') #时间戳转时间，北京时间+8小时
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df=df[['time','open','close','high','low','vol']] #更正排序
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df.set_index(["time"], inplace=True); df.index.name='' #处理索引
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return df
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def get_last_price(code): #获取某只股票最新价格函数04-25
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return get_price(code,count=1,frequency='1m', fields=['close']).close[0]
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def attribute_history(security, count, unit='1d', fields=['open', 'close', 'high', 'low', 'volume', 'money']): #获取历史行情
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return get_price(security = security, end_date = context.current_dt, frequency=unit, fields=fields, fq = fq, count = count)[:-1]
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