def _initialize(self, strategy=None, start=None, end=None, timezone=None, commission=None):
self.start = start
self.end = end
self.commission = commission
self.timezone=timezone
self.portfolio = Portfolio(strategy, start, end, commission,
self.frequency, self.refresh_rate, self.trigger_time)
self.context = self.portfolio.context
# 最小精度
self.context.min_order = {}
for univ in self.universe:
exchange = univ.split('.')[1]
self.context.min_order[exchange] = {}
# print(self.context.min_order)
for univ in self.universe:
exchange = univ.split('.')[1]
symbol = univ.split('.')[0].lower()
min_order_qty, min_order_amount = get_min_order(exchange, symbol)
self.context.min_order[exchange][symbol] = {'min_order_qty':min_order_qty,
'min_order_amount':min_order_amount}
# 记录universe所有的asset
self.universe_assets = list()
for univ in self.universe:
base_asset = univ.split('.')[0].split('/')[0].lower()
quote_asset = univ.split('.')[0].split('/')[1].lower()
if base_asset not in self.universe_assets:
self.universe_assets.append(base_asset)
if quote_asset not in self.universe_assets:
self.universe_assets.append(quote_asset)
self.all_assets = list(set(AccountManager().asset_varieties + self.universe_assets))
# print(self.all_assets)
# # 初始每个账户添加除初始化之外的universe的asset
# for name,account in self.portfolio.accounts.items():
# for asset in universe_assets:
# if asset not in account.current_position.keys():
# account.current_position[asset] = SpotPosition('spot', asset, 0, 0, 0, 0)
self.total_dates = get_total_dates(self.frequency, 1, self.trigger_time, self.start, self.end)
self.strategy_dates = get_total_dates(self.frequency, self.refresh_rate, self.trigger_time, self.start, self.end)
if self.frequency in ['d','1d','day','1day']:
self.day_date=get_day_dates(self.start, self.end,self.refresh_rate)
else:
self.day_date=get_day_dates(self.start, self.end)
# 先输出report的dates信息，画图用
output({"display_type": "strategy",
"dates":self.day_date})
try:
self.initialize(self.context)
except Exception:
output({"display_type": "error",
"error_msg": traceback.format_exc()})
return 1
'granularity':granularity #以秒为单位的时间间隔，１分钟取值60 １小时取值3600 一天取值86400
}
#一次请求获得Ｋ线长度不超过300，可以多次反复请求
r = requests.get(url,params=payload)
klines=json.loads(r.text)
return klines
if __name__=='__main__':
exchange='coinbase'
symbol='btcusd'
freq='1d'
start1='2017-01-01 00:00:00'
end1='2020-05-31 00:00:00'
start2=datetime.strptime(start1,'%Y-%m-%d %H:%M:%S').timestamp()
end2=datetime.strptime(end1,'%Y-%m-%d %H:%M:%S').timestamp()
length1=get_total_dates(freq,1,0,start1,end1)
if freq == '1d':
interval=86400
count=round((end2-start2)/(lenght1-1))
if count>1:
for i in range(count)
data1=kline_coinbase('2017-03-23T08:00:00.000Z','2017-12-23T08:00:00.000Z',86400)
df=data1.sort_index(axis=0,by='date',ascending=True)
years = dict()
for i in range(len(df)):
year = (datetime.strptime(df.iloc[i]['date'],'%Y-%m-%d %H:%M:%S')).year
if year not in years:
years[year] = list()
tempdata1= (df.iloc[i][1:7].values)
def get_data(exchange, symbol, frequency, start_date, end_date, timezone):
data = get_bars(symbol,
exchange,
frequency=frequency,
start_date=start_date,
end_date=end_date,
timezone=timezone)
df_data = pd.DataFrame(data)
# total_ts = get_total_timestamps(start_date, end_date) #只能获得天的日期
total_ts = get_total_dates(frequency, 1, 0, start_date,
end_date) #天或者分钟的日期，
#对df_data清醒数据清洗
if len(df_data) > 1:
if df_data['timestamp'][0] > 0 and df_data['close'][0] > 0:
for x in range(1, len(df_data)):
if df_data['close'][x] == 0:
df_data.iloc[x, 1:6] = df_data.iloc[x - 1, 1:6]
else:
nonzero = df_data[df_data['close'] > 0].index.tolist()
if len(nonzero) > 0:
for n in range(0, nonzero[0]):
df_data.iloc[n, 1:6] = df_data.iloc[nonzero[0], 1:6]
for x in range(1, len(df_data)):
if df_data['high'][x] == 0 and df_data['close'][x] == 0:
df_data.iloc[x, 1:6] = df_data.iloc[x - 1, 1:6]
if len(df_data) == len(total_ts):
df_data['timestamp'] = total_ts
else:
print('data error in cleaning, please wait')
df_date = np.array(df_data['timestamp'], dtype='uint64')
ts_date = []
for i in range(0, len(total_ts)):
temp1 = datetime.strptime(total_ts[i],
'%Y-%m-%d %H:%M:%S').timestamp()
ts_date.append(round(temp1)) #total_ts转化为时间戳形式
ts_date = np.array(ts_date, dtype='uint64')
temp2 = np.zeros([len(ts_date), 5])
df_data1 = pd.DataFrame(
temp2,
index=range(len(ts_date)),
columns=['open', 'high', 'low', 'close', 'volume'])
df_data1.insert(0, 'timestamp',
total_ts) #创建一个完整的dataframe,下面把df_data的值复制进去，再把没有的部分补上
if len(df_data) < len(total_ts):
x = len(df_data) - 1
start1 = 0
while x > 0:
if df_date[x] != ts_date[x]:
x -= 1
elif df_date[x] == ts_date[x]:
start1 = x
break
if start1 > 0:
df_data1.iloc[0:start1, 1:6] = df_data.iloc[0:start1, 1:6]
for j in range(start1, len(ts_date)):
if ts_date[j] in df_date:
loacte1 = np.argwhere(df_date == ts_date[j])
df_data1.iloc[j, 1:6] = df_data.iloc[int(loacte1[0]),
1:6]
else:
if j > 0:
df_data1.iloc[j, 1:6] = df_data1.iloc[j - 1, 1:6]
else:
for j in range(0, len(ts_date)):
if ts_date[j] in df_date:
loacte1 = np.argwhere(df_date == ts_date[j])
df_data1.iloc[j, 1:6] = df_data.iloc[int(loacte1[0]),
1:6]
else:
if j > 0:
df_data1.iloc[j, 1:6] = df_data1.iloc[j - 1, 1:6]
if len(df_data) > len(total_ts):
for j in range(0, len(ts_date)):
if ts_date[j] in df_date:
loacte1 = np.argwhere(df_date == ts_date[j])
df_data1.iloc[j, 1:6] = df_data.iloc[int(loacte1[0]), 1:6]
else:
if j > 0:
df_data1.iloc[j, 1:6] = df_data1.iloc[j - 1, 1:6]
df_data = df_data1
return df_data