如题，我下载了最新的托管者，但是还是会得到这个错误，GetDepth: Invalid depth: {“Info”:null,“Asks”:[{“Price”:2.49,“Amount”:1536.043919},{“Price”:2.5,“Amount”:11024.020949

请问会测试提示：Uncaught RangeError: WebAssembly.Memory(): could not allocate memory 关掉网页，重新回测就好了，这是什么原因呢？

看到可添加的交易所里有ZBG，但是试了下，目前的接口都不能用，看到有通用协议的接入文档，但是貌似挺麻烦的。想问下，现在如果只需要几个简单功能(比如单纯的下单功能)的话，是否有比较简单的实现办法？比如我看到有个exchange.IO是否能用上？（ZBG的交易API貌似只有HTTP的）

不懂编程只能用可视化..请问怎么用blockly可视化编程下期货的市价交易单？

BitMEX 交易所API使用事项 (BitMEX exchange API note) The FMZ platform API Doc [Join us on telegram group](https://t

基本上所有数字货币交易所都支持websocket发送行情，部分交易所支持websocket更新账户信息。相比于rest API, websocket一般具有延时低，频率高，不受平台rest API频率限制等有带你，缺点是有中断问题，处理不直观。关于websocket简介，可以参考我曾经的这篇文章： https://zhuanlan.zhihu.com/p/22693475 本文将主要介绍在FMZ

“strategy_exchange_pairs” => “[3,[43793],[“BTC_USDT”]]”

In trading, a single transaction result with win or loss is not the focus of quantitative traders, so what are they most concerned about? The answer is the result of trading the system after 800 or 10

Over the last few years we’ve looked at various tools to help us identify exploitable patterns in asset prices. In particular we have considered basic econometrics, statistical machine learning and Ba

Mature Python libraries such as matplotlib, pandas and scikit-learn also reduce the necessity to write boilerplate code or come up with our own implementations of well known algorithms. The Foreca

No matter how much time, effort, and money you put into an investment, if you don’t have a predetermined exit strategy, everything can be gone. For this reason, investment guru never invests without k

现在Log(@)可以推送消息到FMZ公众号。 能不能在FMZ公众号发消息给托管者，做对应的get command操作？

Institutional asset managers specialize in a particular asset class, style, sector, or geography, based on their expertise or domain knowledge. This is reflected in the investment products they offer

In this article we are going to consider our first intraday trading strategy. It will be using a classic trading idea, that of “trading pairs”. In this instance we are going to be making use of two Ex

In this article we will make use of the machinery we introduced to carry out research on an actual strategy, namely the Moving Average Crossover on AAPL. Moving Average Crossover Strategy The Movi

In this article I want to introduce you to the methods by which I myself identify profitable algorithmic trading strategies. Our goal today is to understand in detail how to find, evaluate and select

It’s been a while since we’ve considered the event-driven backtester, which we began discussing in this article. In Part VI I described how to code a stand-in ExecutionHandler model that worked for a

Original: FMZ Quant www.fmz.com The “real stuff” of quantitative trading gathering place where you can truly benefit from. NO.1 Warren Buffett’s mentor, Benjamin Graham, once mentioned in the book

In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy. In this article we are going to discuss how to assess the performance of a strategy post-bac

This article continues the discussion of event-driven backtesters in Python. In the previous article we considered a portfolio class hierarchy that handled current positions, generated trading orders

In the previous article on event-driven backtesting we considered how to construct a Strategy class hierarchy. Strategies, as defined here, are used to generate signals, which are used by a portfolio

The discussion of the event-driven backtesting implementation has previously considered the event-loop, the event class hierarchy and the data handling component. In this article a Strategy class hier

In the previous two articles of the series we discussed what an event-driven backtesting system is and the class hierarchy for the Event object. In this article we are going to consider how market dat

In the last article we described the concept of an event-driven backtester. The remainder of this series of articles will concentrate on each of the separate class hierarchies that make up the overall

We’ve spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas. The vectorised nature of pandas ensures that certain operations on large dataset

申请增加kucoin的API，kucoin的api文档写的非常详细。谢谢！

In the first article on successful backtesting we discussed statistical and behavioural biases that affect our backtest performance. We also discussed software packages for backtesting, including Exce

代码如下，huobi在大概五六秒之后，会收到服务器的ping，然后我会发出一个pong，但是一直好像服务器收不到Pong似的，还在一直给我发ping，请教了大神说是给read加-1，但加了还是没用 “` function main() { var ws_hbdm = Dial(“wss://www.hbdm.com/ws?compress=true|compress=gzip&mode=

This article continues the series on quantitative trading, which started with the Beginner’s Guide and Strategy Identification. Both of these longer, more involved articles have been very popular so I

Value at Risk (VaR) for Algorithmic Trading Risk Management Estimating the risk of loss to an algorithmic trading strategy, or portfolio of strategies, is of extreme importance for long-term capital