后面的参数主要有两个,一个是timeframe,也就是你希望变成的timeframe是多少,day还是week;另外一个是compression,就是对bar进行压缩。...=bt.TimeFrame.Minutes ) #timeframe=bt.TimeFrame.Minutes用来指明datafeed...cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days)#加入另外一个新的timeframe的datafeed的时候,就不能是adddata了,...而是之前说的resampling cerebro.run() cerebro.plot(style='bar') timeframe=bt.TimeFrame.Minutes用来指明datafeed...cerebro.resampledata(data0, timeframe=bt.TimeFrame.Days)#加入另外一个新的timeframe的datafeed的时候,就不能是adddata了,
, 'weeks': bt.TimeFrame.Weeks, 'months': bt.TimeFrame.Months, 'years': bt.TimeFrame.Years...'S10', (TimeFrame.Seconds, 15): 'S15', (TimeFrame.Seconds, 30): 'S30', (TimeFrame.Minutes, 1): 'M1',...(TimeFrame.Minutes, 2): 'M3', (TimeFrame.Minutes, 3): 'M3', (TimeFrame.Minutes, 4): 'M4', (TimeFrame.Minutes..., 5): 'M5', (TimeFrame.Minutes, 10): 'M10', (TimeFrame.Minutes, 15): 'M15', (TimeFrame.Minutes, 30):...(TimeFrame.Minutes, 240): 'H4', (TimeFrame.Minutes, 360): 'H6', (TimeFrame.Minutes, 480): 'H8', (TimeFrame.Days
bytes.Buffer 实现了io.Writer接口 tmpl.Execute(os.Stdout, map[string]interface{}{ "Person": "Bob", "Timeframe...": 1, }) 改为 var tmpl = template.Must(template.New("").Parse(`{{.Person}}{{.Timeframe}}`))...buf := new(bytes.Buffer) tmpl.Execute(buf, map[string]interface{}{ "Person": "Bob", "Timeframe
, weekly=bt.TimeFrame.Weeks, monthly=bt.TimeFrame.Months) # Handy dictionary for..., weekly=bt.TimeFrame.Weeks, monthly=bt.TimeFrame.Months) # Handy dictionary for...- smaller timeframe cerebro.adddata(data) # And then the large timeframe cerebro.adddata...dataname=data, timeframe=tframes[args.timeframe], compression=args.compression)...dataname=data, timeframe=tframes[args.timeframe], compression=args.compression)
数组里任意内容; whitelist:compare_key字段的内容一个都没能匹配上whitelist数组里内容; change:在相同query_key条件下,compare_key字段的内容,在 timeframe...范围内 发送变化; frequency:在相同 query_key条件下,timeframe 范围内有num_events个被过滤出 来的异常; spike:在相同query_key条件下,前后两个timeframe...还可以通过threshold_ref设置要求上一个周期数据量的下限,threshold_cur设置要求当前周期数据量的下限,如果数据量不到下限,也不触发; flatline:timeframe 范围内,...:fields字段新出现之前terms_window_size(默认30天)范围内最多的terms_size (默认50)个结果以外的数据; cardinality:在相同 query_key条件下,timeframe...index: es-nginx*,winlogbeat* #时间出发的次数 num_events: 5 #和num_events参数关联,也就是说在4分钟内出发5次会报警 timeframe:
_timeframe tcomp = self.data....现在 TimeFrame(backtrader.TimeFrame)已经扩展,包含了“Ticks”、“MicroSeconds”和“Seconds”的常量和名称。...%f', timeframe=bt.TimeFrame.Ticks, ) # Handy dictionary for the argument timeframe conversion...tframes = dict( ticks=bt.TimeFrame.Ticks, microseconds=bt.TimeFrame.MicroSeconds..., seconds=bt.TimeFrame.Seconds, minutes=bt.TimeFrame.Minutes, daily=bt.TimeFrame.Days
* @author Edison * */ public class TimeFrame extends JFrame { /* * Variables */ private JPanel...DEFAULT_TIME_FORMAT = "HH:mm:ss"; private String time; private int ONE_SECOND = 1000; public TimeFrame...Calendar.getInstance().getTime()); displayArea.setText(time); } } public static void main(String arg[]) { TimeFrame...timeFrame=new TimeFrame(); timeFrame.setVisible(true); } }/* 何问起 hovertree.com */ 继承TimerTask来创建一个自定义的
install gitlab-watchman 工具使用 GitLab Watchman将以全局命令的形式进行安装,可以通过下列方式使用: usage: gitlab-watchman [-h] --timeframe...milestones Search milestones --comments Search comments required arguments: --timeframe...Where to send results 我们可以使用GitLab Watchman来查询所有支持的数据项,并将结果输出至默认Stdout: gitlab-watchman --timeframe...a --all 或者,我们也可以将参数一起提交给搜索命令: gitlab-watchman --timeframe m --commits --milestones --output stream 项目地址
readonly RequestDelegate _next; private readonly int _requestLimit; private readonly TimeSpan _timeFrame...ConcurrentDictionary(); public RateLimitingMiddleware(RequestDelegate next, int requestLimit, TimeSpan timeFrame...) { _next = next; _requestLimit = requestLimit; _timeFrame = timeFrame; }...(_requestCount.TryGetValue(ipAddress, out var count) && (currentTime - TimeSpan.FromMinutes(1)) (requestLimit: 100, timeFrame
下面,我们以一些变量开始: timeframe = '2015-05' sql_transaction = [] connection = sqlite3.connect('{}.db'.format...(timeframe)) c = connection.cursor() timeframe值将成为我们将要使用的数据的年份和月份。...目前为止的代码: import sqlite3 import json from datetime import datetime timeframe = '2015-05' sql_transaction...('-')[0],timeframe), buffering=1000) as f: for row in f: row_counter会不时输出,让我们知道我们在迭代的文件中走了多远,...让我们继续构建这个循环: for timeframe in timeframes: connection = sqlite3.connect('{}.db'.format(timeframe))
(default: False) --resample resample to chosen timeframe (default: False) --timeframe...= bt.TimeFrame.TFrame(args.timeframe) if args.resample or args.replay: datatf = bt.TimeFrame.Ticks...') parser.add_argument('--timeframe', default=bt.TimeFrame.Names[0], choices...问题将按以下方式解决: data = btfeeds.ADataFeed(..., timeframe=bt.TimeFrame.Days) cerebro.adddata(data) cerebro.resampledata...(data, timeframe=bt.TimeFrame.Months) 而后在 策略 中: class MyStrategy(bt.Strategy): def __init__(self)
{Weeks,Months,Years} Timeframe to resample to (default: Weeks) 示例用法(tcal-intra.py...{Days} Timeframe to resample to (default: Days) 示例代码(tcal.py) from __future__ import (absolute_import...=getattr(bt.TimeFrame, args.timeframe)) d1.plotinfo.plotmaster = data0 d1.plotinfo.sameaxis =...=getattr(bt.TimeFrame, args.timeframe)) # d1.plotinfo.plotmaster = data0 # d1.plotinfo.sameaxis...=bt.TimeFrame.Minutes, compression=15) for i in range(args.numfiles): dataname = 'candles
=bt.TimeFrame.Ticks, compression=args.compression) 我们提供相同的timeframe,数据携带的是TimeFrame.Ticks...=bt.TimeFrame.Ticks) cerebro.resampledata(data, timeframe=bt.TimeFrame.Ticks...=backtrader.TimeFrame.Weeks) # to weeks ......: datetime.timedelta(seconds=60), TimeFrame.Seconds: datetime.timedelta(seconds=1), TimeFrame.MicroSeconds..._timeframe] * data.
月在纽约的“冠状病毒”数据: pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload(['Coronavirus'], cat=0, timeframe..., state): pytrends = TrendReq(hl='en-US', tz=360) pytrends.build_payload([key_word], cat=0, timeframe
就像这样: data0 = MyDataFeed(dataname='xxx', timeframe=bt.TimeFrame.Days, compression=1) data0.addfilter(...现在可以使用plotylimited来控制行为,例如: ... data0 = MyDataFeed(dataname='xxx', timeframe=bt.TimeFrame.Days, compression...例如: ... data0 = MyDataFeed(dataname='xxx', timeframe=bt.TimeFrame.Days, compression=1) data0.plotinfo.plotlog...(data) for ticker in mytickers[1:] data = MyDataFeed(dataname=ticker, timeframe=..., compression=...=bt.TimeFrame.Days, compression=1, sessionstart=datetime.time(9, 0), sessionend
先使用ccxt获取交易所的实例,然后获取历史k线,得到的数据使用dataframe格式接受 huobipro.fetch_ohlcv(symbol=symbol,limit=limit_num,timeframe...=timeframe) 然后利用pandas提供的函数计算MA, df['median_short']=df['close'].rolling(n_short,min_periods=1).mean
= durationSeconds * 60 // 定义 CMTime 即请求缩略图的时间间隔 for i in 0...Int(totalFrames) { let timeFrame...= CMTimeMake(value: Int64(i), timescale: 60) let timeValue = NSValue(time: timeFrame)
name 必须是独一的,不然会报错,这个定义完成之后,会成为报警的标题 name: nginx-access-alert #配置的是frequency,需要两个条件满足,在相同 query_key条件下,timeframe...prod-%Y-%m-%d use_strftime_index: true #时间触发的次数 num_events: 10 #和num_events参数关联,也就是说1分钟内出现10次会报警 timeframe...name 必须是独一的,不然会报错,这个定义完成之后,会成为报警的标题 name: java-prod-alert #配置的是frequency,需要两个条件满足,在相同 query_key条件下,timeframe...-prod-%Y-%m-%d use_strftime_index: true #时间触发的次数 num_events: 10 #和num_events参数关联,也就是说1分钟内出现10次会报警 timeframe
. # the frequency rule type alerts when num_events events occur with timeframe time type: frequency...# (Required, frequency specific) # Alert when this many documents matching the query occur within a timeframe...Required, frequency specific) # num_events must occur within this amount of time to trigger an alert timeframe...5 # (Required) # Type of alert. # the frequency rule type alerts when num_events events occur with timeframe...# (Required, frequency specific) # Alert when this many documents matching the query occur within a timeframe
=bt.TimeFrame.NoTimeFrame) cerebro.addanalyzer(bt.analyzers.TimeReturn, data=data0, _name='benchmark...', timeframe=bt.TimeFrame.NoTimeFrame) # Add TimeReturn Analyzers fot the...annuyl returns cerebro.addanalyzer(bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.Years) #...Add a SharpeRatio cerebro.addanalyzer(bt.analyzers.SharpeRatio, timeframe=bt.TimeFrame.Years,...**dkwargs) data.addfilter(DayStepsReplayFilter) cerebro.replaydata(data, timeframe=bt.TimeFrame.Days
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