def fetch_():
ts.set_token(cfg.get_datayes_key())
eq = ts.Equity()
df = eq.Equ(equTypeCD='A', listStatusCD='L', field='ticker')
df['ticker'] = df['ticker'].map(lambda x: str(x).zfill(6))
start, end = '20150901', '20160326'
# thread can not make full use of cpu
for i, row in df.iterrows():
csv = cfg.get_ratio_table_path(row['ticker'], start, end)
if os.path.exists(csv):
print("{0}/{1} {2} exists.".format(i, len(df.index), row['ticker']))
continue
# code_queue.put(row['ticker'])
# code_list.append(str(row['ticker']))
proc_queue.put(row['ticker'])
# for i in range(3):
# thread = ExportThread(thread_id, code_queue, start, end)
# thread.start()
# threads.append(thread)
# thread_id += 1
#
# get_code_and_export(0, code_queue, start, end)
#
# for t in threads:
# t.join()
#
# print("Exit main thread.")
processes = 4
for i in range(processes):
p = ExportProcess(i+1, start, end, proc_queue)
p.start()
procs.append(p)
for p in procs:
p.join()
print("Exit main")
def export_ratio_table(code, start, end, thread_id):
# queue_lock.acquire()
ts.set_token(cfg.get_datayes_key())
mkt = ts.Market()
# print("exporting " + code + " from " + start + " to " + end)
st = time.time()
df = mkt.MktEqud(ticker=code, beginDate=start, endDate=end,
field='ticker,tradeDate,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice')
print(" Thread {0} fetch online: {1}".format(thread_id, time.time()-st))
# queue_lock.release()
# df = ts.get_h_data(code, start, end)
# print(df)
wave_ratio_df = pd.DataFrame(columns=["max_ratio", "min_ratio"])
for i, row in df.iterrows():
dict = wv.calc_wave_ratio(row["preClosePrice"], row["openPrice"],
row["highestPrice"], row["lowestPrice"])
wave_ratio_df.loc[row["tradeDate"]] = dict
st = time.time()
idx_col = wv.calc_ratio_table_index_and_columns(max_ratio=0.03, min_ratio=-0.03)
index, columns = idx_col["index"], idx_col["columns"]
ratio_table = wv.calc_ratio_table(wave_ratio_df, index, columns)
print(" Thread {0} calc ratio table: {1}".format(thread_id, time.time()-st))
st = time.time()
length_ratio_df = wv.calc_length_ratio(ratio_table, len(wave_ratio_df.index))
print(" Thread {0} calc length ratio: {1}".format(thread_id, time.time()-st))
# write csv
st = time.time()
ratio_table.to_csv(cfg.get_ratio_table_path(code, start, end))
length_ratio_df.to_csv(cfg.get_length_ratio_path(code, start, end))
# print(" save csv: {0}".format(time.time()-st))
return length_ratio_df
# -*- coding: utf-8 -*-
import config as cfg
import tushare as ts
import numpy as np
import pandas as pd
import os
ts.set_token(cfg.get_datayes_key())
def merge_length(start, end):
lens = np.arange(start=0.0100, stop=0.0200, step=0.0001)
columns = []
for x in lens:
columns.append("%.4f" % x)
print(columns)
df = pd.DataFrame(columns=columns)
eq = ts.Equity()
all = eq.Equ(equTypeCD='A', listStatusCD='L', field='ticker')
all['ticker'] = all['ticker'].map(lambda x: str(x).zfill(6))
for i, row in all.iterrows():
path = cfg.get_length_ratio_path(row['ticker'], start, end)
try:
if os.path.exists(path):
len_df = pd.DataFrame.from_csv(path)
len_df.index = len_df.index.map(lambda x: "%.4f" % x)
# print(len_df.index)
for len in columns:
# print(len_df.loc[len, 'ratio'])
# -*- coding: utf-8 -*-
import config as cfg
import tushare as ts
import numpy as np
import pandas as pd
import os
ts.set_token(cfg.get_datayes_key())
def merge_length(start, end):
lens = np.arange(start=0.0100, stop=0.0200, step=0.0001)
columns = []
for x in lens:
columns.append("%.4f" % x)
print(columns)
df =pd.DataFrame(columns=columns)
eq = ts.Equity()
all = eq.Equ(equTypeCD='A', listStatusCD='L', field='ticker')
all['ticker'] = all['ticker'].map(lambda x:str(x).zfill(6))
for i, row in all.iterrows():
path = cfg.get_length_ratio_path(row['ticker'], start, end)
try:
if os.path.exists(path):
len_df = pd.DataFrame.from_csv(path)
len_df.index = len_df.index.map(lambda x : "%.4f" % x)
# print(len_df.index)
for len in columns:
# print(len_df.loc[len, 'ratio'])