#掘金3的常见指标实现 BY赏玫瑰LUOW。（如有出错请指出）

coding=utf-8

from future import print_function, absolute_import, unicode_literals

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import talib

#ta.set_compatibility(1)

from gm.api import *

def init(context):

context.goods交易的品种

context.goods = [‘SZSE.000063’]

订阅context.goods里面的品种, bar频率为1min

subscribe(symbols=context.goods, frequency=‘1d’, count=500)

止损的比例区间

def on_bar(context, bars):

bar = bars[0]

mysymbol = bar[‘symbol’]

recent_data = context.data(symbol=mysymbol, frequency=‘1d’, count=500, fields=‘symbol,open,close,high,low,bob’)

today=context.now #获取当天日期时间

close=recent_data.close.values #获取收盘列表

high=recent_data.high.values #最高价列表

low=recent_data.low.values #最低价列表

print(’\n’)

下面显示当天时间，交易代码，最新收盘价取值来自close[-1。如果前一天收盘价为close[-2],前两天收盘价为close[-3]

print (today,mysymbol,‘收盘’,round(close[-1],3))

均线的获取

MA5 = talib.MA(close, timeperiod=5) # 获取5天均线列表

MA10 = talib.MA(close, timeperiod=10) # 获取10天均线列表

MA20 = talib.MA(close, timeperiod=20) # 获取20天均线列表

MA60 = talib.MA(close, timeperiod=60) # 获取60天均线列表

print (‘5天均线 :’, round(MA5[-1],3))

print (‘10天均线 :’, round(MA10[-1],3))

print (‘20天均线 :’, round(MA20[-1],3))

print (‘60天均线 :’, round(MA60[-1],3))

macd 指标 2026重试OK

macddiff, macddea, macd = talib.MACDEXT(recent_data.close.values, fastperiod=12, fastmatype=1, slowperiod=26, slowmatype=1, signalperiod=9, signalmatype=1)

macd = macd * 2

print ('macd is ',round(macd[-1],3),‘diff is’,round(macddiff[-1],3),‘dea is’,round(macddea[-1],3))

rsi 威廉指标 2026已经OK

rsi6=talib.RSI(close, timeperiod=6)

rsi12=talib.RSI(close, timeperiod=12)

rsi24=talib.RSI(close, timeperiod=24)

print (‘rsi6’,round(rsi6[-1],2),‘rsi12’,round(rsi12[-1],2),‘rsi24’,round(rsi24[-1],2))

WILLR威廉指标 2026已经OK 。需要用abs函数返回绝对值即取正数

will10 = abs(talib.WILLR(high, low, close, timeperiod=10))

will6 = abs(talib.WILLR(high, low, close, timeperiod=6))

will20 = abs(talib.WILLR(high, low, close, timeperiod=20))

will80 = abs(talib.WILLR(high, low, close, timeperiod=80))

print('wr10 ',round(will10[-1],2),'wr6 ',round(will6[-1],2),'wr20 ',round(will20[-1],2),'wr80 ',round(will80[-1]))

KDJ指标。引用下面的cal_kdj_vector(df_data)函数得出，与通达信同花顺数值一样 2026测试 OK。

mykdj= cal_kdj_vector(recent_data)

K=mykdj[‘K’].values

D=mykdj[‘D’].values

J=mykdj[‘J’].values

print (‘K is’,round(K[-1],2),‘D is’,round(D[-1],2),‘J is’,round(J[-1],2))

#LWR 是KDJ的补数。2026测试OK

lwr1=100-K

lwr2=100-D

lwr3=100-J

print (‘LWR1’,round(lwr1[-1],2),‘LWR2’,round(lwr2[-1],2),'LWR3 ',round(lwr3[-1],2))

def cal_kdj_vector(df_data):

low_list = df_data[‘low’].rolling(9, min_periods=1).min()

high_list = df_data[‘high’].rolling(9, min_periods=1).max()

rsv = (df_data[‘close’] - low_list) / (high_list - low_list) * 100

df_data[‘K’] = rsv.ewm(com=2, adjust=False).mean()

df_data[‘D’] = df_data[‘K’].ewm(com=2, adjust=False).mean()

df_data[‘J’] = 3 * df_data[‘K’] - 2 * df_data[‘D’]

return df_data

if name == ‘main’:

run(strategy_id=‘strategy_id’,

filename=‘main.py’,

mode=MODE_BACKTEST,

token=‘token_id’,

backtest_start_time=‘-07-23 09:15:00’,

backtest_end_time=‘-07-24 15:01:00’,

backtest_adjust=ADJUST_PREV,

backtest_initial_cash=500000,

backtest_commission_ratio=0.0001,

backtest_slippage_ratio=0.0001)