def 每日浮动收益统计(symbol:str, Type:str):
from data_reader import get_index_day, get_stock_day, get_index_future_day, get_comm_future_day
import pymssql
start_date = str(date_list.iloc[1])[:10]
end_date = str(date_list.iloc[-1])[:10]
print("Fetching data from SQL database...")
if Type=='stock':
close = get_stock_day(symbol, start_date, end_date, freq = '1D')
elif Type=='index':
close = get_index_day(symbol, start_date, end_date, freq = '1D')
elif Type=='index_future':
close = get_index_future_day(symbol, start_date, end_date, freq = '1D')
elif Type=='comm_future':
close = get_comm_future_day(symbol, start_date, end_date, freq = '1D')
elif Type=='coin':
conn = pymssql.connect(server='192.168.0.28', port=1433, user='******', password='******', database='rawdata',charset = 'GBK')
SQL = "select * from coin.dbo.coin_1min where "+"date between "+"\'"+start_date+"\' and \'" + end_date+"\'"
temp_data = pd.read_sql(SQL, conn)
close = temp_data.set_index('date')
close = close.resample('1D').last()
elif Type[:3] == 'AIP':
if Type[-5:] == 'stock':
close = get_stock_day(symbol, start_date, end_date, freq = '1D')
elif Type[-5:] == 'index':
close = get_index_day(symbol, start_date, end_date, freq = '1D')
else:raise TypeError('Type "{}" is NOT acceptable, please check your input.'.format(Type))
else:
raise TypeError('Type "{}" is NOT acceptable, please check your input.'.format(Type))
close.index = close.index + pd.Timedelta(24, unit='h')
record = []
截止当日的累计盈利, comfirmed_profit = 0, 0
for day in close.index:
float_profit = 0
for deal in deal_list:
if deal.is_valid(day):
float_profit += deal.cal_floating(close.loc[day]['sclose'])
deal.floating_profit=deal.cal_floating(close.loc[day]['sclose'])
elif day.date()==deal.end.date():
#如果是当天结束的，当天确认收益
deal.confirm_profit()
comfirmed_profit+=deal.profit
#deal_list.remove(deal)
截止当日的累计盈利 = comfirmed_profit + float_profit
#print(day, int(float_profit), int(comfirmed_profit), int(截止当日的累计盈利),sep='\t')
record.append((day, float_profit, comfirmed_profit, 截止当日的累计盈利))
ans=pd.DataFrame(record,columns=('date','floating_profit','comfirmed_profit','accumulated_profit'))
ans=ans.set_index('date')
if Type.endswith('future'):
choice = input("You are using futures;\nDo you want to multiply amount by 200?\nInput 1 for YES, 0 for NO: ")
if choice=='0': future_multiplier = 1
else: future_multiplier = 200
ans[['floating_profit','comfirmed_profit','accumulated_profit']]*=future_multiplier
ans['当日盈亏']=ans.accumulated_profit.diff()
return ans
def 每日浮动收益统计(symbol='', Type='stock'):
from data_reader import get_index_day, get_stock_day, get_index_future_day
start_date = str(date_list[0])[:10]
end_date = str(date_list[-1])[:10]
# 如果未指定代码，则从ts原始文件中选取
if symbol=='': symbol = pd.read_excel(f, sheet_name=-1,header=2).columns[1]
if Type=='stock':
close = get_stock_day(symbol, start_date, end_date, freq = '1D')
elif Type=='index':
close = get_index_day(symbol, start_date, end_date, freq = '1D')
elif Type=='future':
close = get_index_future_day(symbol, start_date, end_date, freq = '1D')
close.index=close.index+pd.Timedelta(15,unit='h')
record=[]
截止当日的累计盈利=0
comfirmed_profit=0
for day in close.index:
float_profit=0
for deal in deal_list:
if deal.is_valid(day):
float_profit += deal.cal_floating(close.loc[day]['close'])
deal.floating_profit=deal.cal_floating(close.loc[day]['close'])
elif day.date()==deal.end.date():
#如果是当天结束的，当天确认收益
deal.confirm_profit()
comfirmed_profit+=deal.profit
#deal_list.remove(deal)
截止当日的累计盈利 = comfirmed_profit + float_profit
#print(day, int(float_profit), int(comfirmed_profit), int(截止当日的累计盈利),sep='\t')
record.append((day, (float_profit), (comfirmed_profit), (截止当日的累计盈利)))
ans=pd.DataFrame(record,columns=('date','floating_profit','comfirmed_profit','accumlated_profit'))
ans=ans.set_index('date')
ans['当日盈亏']=ans.accumlated_profit.diff()
return ans
def 每日浮动收益统计(symbol:str, Type:str):
from data_reader import get_index_day, get_stock_day, get_index_future_day, get_comm_future_day
start_date = str(date_list[0])[:10]
end_date = str(date_list[-1])[:10]
if Type=='stock':
close = get_stock_day(symbol, start_date, end_date, freq = '1D')
elif Type=='index':
close = get_index_day(symbol, start_date, end_date, freq = '1D')
elif Type=='index_future':
close = get_index_future_day(symbol, start_date, end_date, freq = '1D')
elif Type=='comm_future':
close = get_comm_future_day(symbol, start_date, end_date, freq = '1D')
else:
print('Type is NOT acceptable, please check your input.')
quit()
close.index = close.index + pd.Timedelta(15, unit='h')
record = []
截止当日的累计盈利, comfirmed_profit = 0, 0
for day in close.index:
float_profit = 0
for deal in deal_list:
if deal.is_valid(day):
float_profit += deal.cal_floating(close.loc[day]['sclose'])
deal.floating_profit=deal.cal_floating(close.loc[day]['sclose'])
elif day.date()==deal.end.date():
#如果是当天结束的，当天确认收益
deal.confirm_profit()
comfirmed_profit+=deal.profit
#deal_list.remove(deal)
截止当日的累计盈利 = comfirmed_profit + float_profit
#print(day, int(float_profit), int(comfirmed_profit), int(截止当日的累计盈利),sep='\t')
record.append((day, float_profit, comfirmed_profit, 截止当日的累计盈利))
ans=pd.DataFrame(record,columns=('date','floating_profit','comfirmed_profit','accumlated_profit'))
ans=ans.set_index('date')
if Type.endswith('future'):
choice = input("You are using futures;\nDo you want to multiply amount by 200?\nInput 1 for YES, 0 for NO: ")
if choice=='0': future_multiplier = 1
else: future_multiplier = 200
ans[['floating_profit','comfirmed_profit','accumlated_profit']]*=future_multiplier
ans['当日盈亏']=ans.accumlated_profit.diff()
return ans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
import sklearn as skl
import talib
from sklearn.linear_model import LinearRegression
import pymssql
from WindPy import w
from WindPy import *
N = 60
year_start = 2012
year_end = 2017
data = get_stock_day('600000.SH', '2010-01-01', '2017-12-31')
w.start()
rawdata = w.wsd("600000.SH", "dealnum", "2010-01-01", "2017-12-31", "")
rawdata = pd.DataFrame(rawdata.Data[0],
index=rawdata.Times,
columns=['dealnum'])
data = pd.concat([data, rawdata], axis=1)
data['D'] = data['amt'] / data['dealnum']
data = data['2012':]
# %% 选股
def collect_data():
data = pd.DataFrame()
def create_ta_data(stock):
print("Processing", stock)
if stock in stock_pool:
SS = rd.get_stock_day(stock, "2010-01-01", "2019-01-01", "1D")
SS = SS / SS.iloc[0]
Return = holding_return[stock]
target = holding_return.rank(axis=1)[stock]
xlabel = label(target)
target = pd.DataFrame(target)
target["label"] = xlabel
target = target[["label"]]
elif stock == "benchmark":
SS = benchmark
SS.columns = ["sclose", "sopen", "high", "low", "vol"]
MA = pd.DataFrame()
# 计算10-90日均线
for i in range(10, 50, 10):
local_MA = ta.MA(SS.sclose, timeperiod=i)
local_MA.name = stock + "_MA_" + str(i)
MA = pd.concat([MA, local_MA], axis=1)
MACD1, MACD2, XX = ta.MACD(SS.sclose)
MACD = pd.concat([MACD1, MACD2], axis=1)
MACD.columns = [stock + "_MACD1", stock + "_MACD2"]
ADX = ta.ADX(SS.high, SS.low, SS.sclose) / 25
ADXR = ta.ADXR(SS.high, SS.low, SS.sclose) / 25
aroondown, aroonup = ta.AROON(SS.high, SS.low)
aroondown = aroondown / 100
aroonup = aroonup / 100
ATR = ta.ATR(SS.high, SS.low, SS.sclose)
Bupper, Bmiddle, Blower = ta.BBANDS(SS.sclose)
group1 = pd.concat(
[ADX, ADXR, aroondown, aroonup, ATR, Bupper, Bmiddle, Blower], axis=1
)
group1.columns = [
stock + "_ADX",
stock + "_ADXR",
stock + "_aroondown",
stock + "_aroonup",
stock + "_ATR",
stock + "_Bupper",
stock + "_Bmiddle",
stock + "_Blower",
]
BOP = ta.BOP(SS.sopen, SS.high, SS.low, SS.sclose)
CCI = ta.CCI(SS.high, SS.low, SS.sclose)
CCI = CCI / 100
CMO = ta.CMO(SS.sclose)
CMO = CMO / 100
DX = ta.DX(SS.high, SS.low, SS.sclose)
EMA = ta.EMA(SS.sclose)
KAMA = ta.KAMA(SS.sclose)
MFI = ta.MFI(SS.high, SS.low, SS.sclose, SS.vol)
MFI = MFI / 100
MOM = ta.MOM(SS.sclose)
RSI = ta.RSI(SS.sclose)
RSI = (RSI - 50) / 100
group2 = pd.concat([BOP, CCI, CMO, DX, EMA, KAMA, MFI, MOM, RSI], axis=1)
group2.columns = [
stock + "_BOP",
stock + "_CCI",
stock + "_CMO",
stock + "_DX",
stock + "_EMA",
stock + "_KAMA",
stock + "_MFI",
stock + "_MOM",
stock + "_RSI",
]
SAR = ta.SAR(SS.high, SS.low)
TRANGE = ta.TRANGE(SS.high, SS.low, SS.sclose)
TRIMA = ta.TRIMA(SS.sclose)
group3 = pd.concat([SAR, TRANGE, TRIMA], axis=1)
group3.columns = [stock + "_SAR", stock + "_TRANGE", stock + "_TRIMA"]
SS.columns = [
stock + "_close",
stock + "_open",
stock + "_high",
stock + "_low",
stock + "_vol",
]
raw_ta = pd.concat([SS, MA, MACD, group1, group2, group3], axis=1)
if stock in stock_pool:
raw_ta = pd.concat([raw_ta, target, Return], axis=1)
return raw_ta
def 每日浮动收益统计(symbol: str, Type: str):
from data_reader import get_index_day, get_stock_day, get_index_future_day, get_comm_future_day
import pymssql
start_date = str(date_list.iloc[1] -
pd.Timedelta(30, 'd'))[:10] # 早30天，为了使用RSI数据
end_date = str(date_list.iloc[-1])[:10]
print("[Time Range]", start_date, "~", end_date)
print("Fetching data from SQL database...")
if Type == 'stock':
close = get_stock_day(symbol, start_date, end_date, freq='1D')
elif Type == 'index':
close = get_index_day(symbol, start_date, end_date, freq='1D')
elif Type == 'index_future':
close = get_index_future_day(symbol, start_date, end_date, freq='1D')
elif Type == 'comm_future':
close = get_comm_future_day(symbol, start_date, end_date, freq='1D')
elif Type == 'coin':
conn = pymssql.connect(server='192.168.0.28',
port=1433,
user='******',
password='******',
database='rawdata',
charset='GBK')
SQL = "select * from coin.dbo.coin_1min where " + "date between " + "\'" + start_date + "\' and \'" + end_date + "\'"
temp_data = pd.read_sql(SQL, conn)
close = temp_data.set_index('date')
close = close.resample('1D').last()
elif Type == 'AIP':
close = get_stock_day(symbol, start_date, end_date, freq='1D')
else:
print('Type is NOT acceptable, please check your input.')
quit()
close.index = close.index + pd.Timedelta(24, unit='h')
print("SQL data loaded...")
print("统计当日盈亏中...")
t0 = time.time()
record = []
截止当日的累计盈利, comfirmed_profit = 0, 0
for day in close.index[30:]: #close数据提前了30天
float_profit, 累计投入 = 0, 0
for deal in deal_list:
if (deal.start < day) and (deal.end > day):
累计投入 += deal.investment
if deal.is_valid(day):
float_profit += deal.cal_floating(close.loc[day]['sclose'])
deal.floating_profit = deal.cal_floating(
close.loc[day]['sclose'])
elif day.date() == deal.end.date():
#如果是当天结束的，当天确认收益
deal.confirm_profit()
comfirmed_profit += deal.profit
#deal_list.remove(deal)
截止当日的累计盈利 = comfirmed_profit + float_profit
#print(day, int(float_profit), int(comfirmed_profit), int(截止当日的累计盈利),sep='\t')
record.append((day, float_profit, comfirmed_profit, 截止当日的累计盈利, 累计投入))
ans = pd.DataFrame(record,
columns=('date', 'floating_profit', 'comfirmed_profit',
'accumulated_profit',
'accumulated_investment'))
ans = ans.set_index('date')
if Type.endswith('future'):
choice = input(
"You are using futures;\nDo you want to multiply amount by 200?\nInput 1 for YES, 0 for NO: "
)
if choice == '0': future_multiplier = 1
else: future_multiplier = 200
ans[['floating_profit', 'comfirmed_profit',
'accumulated_profit']] *= future_multiplier
ans['当日盈亏'] = ans.accumulated_profit.diff()
t1 = time.time()
tpy = t1 - t0
print('统计当日盈亏已完成，用时 %5.3f 秒' % tpy)
return ans, close
# worths, stock_close = {}, {}
# for index in STOCK_POOL:
# print("----- Now processing {} ...".format(index))
# s.read_files(FILEPATH + index + '.xlsx')
# s.start()
# s.AIP_net()
# s.generate_profit_curve()
# print("----- Processed successfully! -----", end='\n\n')
# worths[index]=s.ans
# stock_close[index]=s.stock_close.sclose
# start_date = worths[index].index[0]
#%% 获取收盘数据
price = {}
for symbol in STOCK_POOL:
temp = get_stock_day(symbol, START_DATE, END_DATE, '1D')
price[symbol] = temp.sclose
price = pd.DataFrame(price)
price.fillna(method='ffill', inplace=True)
price_diff = price.diff()
price_pct_chg = price.pct_change()
#%% 计算相对强弱指数RSI
RSI_Duration = 12
rsi = {}
for index in STOCK_POOL:
rsi[index] = talib.RSI(price[index].shift(-1), RSI_Duration)
rsi = pd.DataFrame(rsi)
#%% 分配仓位
# 总共买3个标的