diff --git a/sources/tech/20200415 How to automate your cryptocurrency trades with Python.md b/sources/tech/20200415 How to automate your cryptocurrency trades with Python.md deleted file mode 100644 index 01ae887940..0000000000 --- a/sources/tech/20200415 How to automate your cryptocurrency trades with Python.md +++ /dev/null @@ -1,424 +0,0 @@ -[#]: collector: (lujun9972) -[#]: translator: (wyxplus) -[#]: reviewer: ( ) -[#]: publisher: ( ) -[#]: url: ( ) -[#]: subject: (How to automate your cryptocurrency trades with Python) -[#]: via: (https://opensource.com/article/20/4/python-crypto-trading-bot) -[#]: author: (Stephan Avenwedde https://opensource.com/users/hansic99) - -How to automate your cryptocurrency trades with Python -====== -In this tutorial, learn how to set up and use Pythonic, a graphical -programming tool that makes it easy for users to create Python -applications using ready-made function modules. -![scientific calculator][1] - -Unlike traditional stock exchanges like the New York Stock Exchange that have fixed trading hours, cryptocurrencies are traded 24/7, which makes it impossible for anyone to monitor the market on their own. - -Often in the past, I had to deal with the following questions related to my crypto trading: - - * What happened overnight? - * Why are there no log entries? - * Why was this order placed? - * Why was no order placed? - - - -The usual solution is to use a crypto trading bot that places orders for you when you are doing other things, like sleeping, being with your family, or enjoying your spare time. There are a lot of commercial solutions available, but I wanted an open source option, so I created the crypto-trading bot [Pythonic][2]. As [I wrote][3] in an introductory article last year, "Pythonic is a graphical programming tool that makes it easy for users to create Python applications using ready-made function modules." It originated as a cryptocurrency bot and has an extensive logging engine and well-tested, reusable parts such as schedulers and timers. - -### Getting started - -This hands-on tutorial teaches you how to get started with Pythonic for automated trading. It uses the example of trading [Tron][4] against [Bitcoin][5] on the [Binance][6] exchange platform. I choose these coins because of their volatility against each other, rather than any personal preference. - -The bot will make decisions based on [exponential moving averages][7] (EMAs). - -![TRX/BTC 1-hour candle chart][8] - -TRX/BTC 1-hour candle chart - -The EMA indicator is, in general, a weighted moving average that gives more weight to recent price data. Although a moving average may be a simple indicator, I've had good experiences using it. - -The purple line in the chart above shows an EMA-25 indicator (meaning the last 25 values were taken into account). - -The bot monitors the pitch between the current EMA-25 value (t0) and the previous EMA-25 value (t-1). If the pitch exceeds a certain value, it signals rising prices, and the bot will place a buy order. If the pitch falls below a certain value, the bot will place a sell order. - -The pitch will be the main indicator for making decisions about trading. For this tutorial, it will be called the _trade factor_. - -### Toolchain - -The following tools are used in this tutorial: - - * Binance expert trading view (visualizing data has been done by many others, so there's no need to reinvent the wheel by doing it yourself) - * Jupyter Notebook for  data-science tasks - * Pythonic, which is the  overall framework - * PythonicDaemon as the  pure runtime (console- and Linux-only) - - - -### Data mining - -For a crypto trading bot to make good decisions, it's essential to get open-high-low-close ([OHLC][9]) data for your asset in a reliable way. You can use Pythonic's built-in elements and extend them with your own logic. - -The general workflow is: - - 1. Synchronize with Binance time - 2. Download OHLC data - 3. Load existing OHLC data from the file into memory - 4. Compare both datasets and extend the existing dataset with the newer rows - - - -This workflow may be a bit overkill, but it makes this solution very robust against downtime and disconnections. - -To begin, you need the **Binance OHLC Query** element and a **Basic Operation** element to execute your own code. - -![Data-mining workflow][10] - -Data-mining workflow - -The OHLC query is set up to query the asset pair **TRXBTC** (Tron/Bitcoin) in one-hour intervals. - -![Configuration of the OHLC query element][11] - -Configuring the OHLC query element - -The output of this element is a [Pandas DataFrame][12]. You can access the DataFrame with the **input** variable in the **Basic Operation** element. Here, the **Basic Operation** element is set up to use Vim as the default code editor. - -![Basic Operation element set up to use Vim][13] - -Basic Operation element set up to use Vim - -Here is what the code looks like: - - -``` -import pickle, pathlib, os -import pandas as pd - -outout = None - -if isinstance(input, pd.DataFrame): -    file_name = 'TRXBTC_1h.bin' -    home_path = str(pathlib.Path.home()) -    data_path = os.path.join(home_path, file_name) - -    try: -        df = pickle.load(open(data_path, 'rb')) -        n_row_cnt = df.shape[0] -        df = pd.concat([df,input], ignore_index=True).drop_duplicates(['close_time']) -        df.reset_index(drop=True, inplace=True) -        n_new_rows = df.shape[0] - n_row_cnt -        log_txt = '{}: {} new rows written'.format(file_name, n_new_rows) -    except: -        log_txt = 'File error - writing new one: {}'.format(e) -        df = input - -    pickle.dump(df, open(data_path, "wb" )) -    output = df -``` - -First, check whether the input is the DataFrame type. Then look inside the user's home directory (**~/**) for a file named **TRXBTC_1h.bin**. If it is present, then open it, concatenate new rows (the code in the **try** section), and drop overlapping duplicates. If the file doesn't exist, trigger an _exception_ and execute the code in the **except** section, creating a new file. - -As long as the checkbox **log output** is enabled, you can follow the logging with the command-line tool **tail**: - - -``` -`$ tail -f ~/Pythonic_2020/Feb/log_2020_02_19.txt` -``` - -For development purposes, skip the synchronization with Binance time and regular scheduling for now. This will be implemented below. - -### Data preparation - -The next step is to handle the evaluation logic in a separate grid; therefore, you have to pass over the DataFrame from Grid 1 to the first element of Grid 2 with the help of the **Return element**. - -In Grid 2, extend the DataFrame by a column that contains the EMA values by passing the DataFrame through a **Basic Technical Analysis** element. - -![Technical analysis workflow in Grid 2][14] - -Technical analysis workflow in Grid 2 - -Configure the technical analysis element to calculate the EMAs over a period of 25 values. - -![Configuration of the technical analysis element][15] - -Configuring the technical analysis element - -When you run the whole setup and activate the debug output of the **Technical Analysis** element, you will realize that the values of the EMA-25 column all seem to be the same. - -![Missing decimal places in output][16] - -Decimal places are missing in the output - -This is because the EMA-25 values in the debug output include just six decimal places, even though the output retains the full precision of an 8-byte float value. - -For further processing, add a **Basic Operation** element: - -![Workflow in Grid 2][17] - -Workflow in Grid 2 - -With the **Basic Operation** element, dump the DataFrame with the additional EMA-25 column so that it can be loaded into a Jupyter Notebook; - -![Dump extended DataFrame to file][18] - -Dump extended DataFrame to file - -### Evaluation logic - -Developing the evaluation logic inside Juypter Notebook enables you to access the code in a more direct way. To load the DataFrame, you need the following lines: - -![Representation with all decimal places][19] - -Representation with all decimal places - -You can access the latest EMA-25 values by using [**iloc**][20] and the column name. This keeps all of the decimal places. - -You already know how to get the latest value. The last line of the example above shows only the value. To copy the value to a separate variable, you have to access it with the **.at** method, as shown below. - -You can also directly calculate the trade factor, which you will need in the next step. - -![Buy/sell decision][21] - -Buy/sell decision - -### Determine the trading factor - -As you can see in the code above, I chose 0.009 as the trade factor. But how do I know if 0.009 is a good trading factor for decisions? Actually, this factor is really bad, so instead, you can brute-force the best-performing trade factor. - -Assume that you will buy or sell based on the closing price. - -![Validation function][22] - -Validation function - -In this example, **buy_factor** and **sell_factor** are predefined. So extend the logic to brute-force the best performing values. - -![Nested for loops for determining the buy and sell factor][23] - -Nested _for_ loops for determining the buy and sell factor - -This has 81 loops to process (9x9), which takes a couple of minutes on my machine (a Core i7 267QM). - -![System utilization while brute forcing][24] - -System utilization while brute-forcing - -After each loop, it appends a tuple of **buy_factor**, **sell_factor**, and the resulting **profit** to the **trading_factors** list. Sort the list by profit in descending order. - -![Sort profit with related trading factors in descending order][25] - -Sort profit with related trading factors in descending order - -When you print the list, you can see that 0.002 is the most promising factor. - -![Sorted list of trading factors and profit][26] - -Sorted list of trading factors and profit - -When I wrote this in March 2020, the prices were not volatile enough to present more promising results. I got much better results in February, but even then, the best-performing trading factors were also around 0.002. - -### Split the execution path - -Start a new grid now to maintain clarity. Pass the DataFrame with the EMA-25 column from Grid 2 to element 0A of Grid 3 by using a **Return** element. - -In Grid 3, add a **Basic Operation** element to execute the evaluation logic. Here is the code of that element: - -![Implemented evaluation logic][27] - -Implemented evaluation logic - -The element outputs a **1** if you should buy or a **-1** if you should sell. An output of **0** means there's nothing to do right now. Use a **Branch** element to control the execution path. - -![Branch element: Grid 3 Position 2A][28] - -Branch element: Grid 3, Position 2A - -Due to the fact that both **0** and **-1** are processed the same way, you need an additional Branch element on the right-most execution path to decide whether or not you should sell. - -![Branch element: Grid 3 Position 3B][29] - -Branch element: Grid 3, Position 3B - -Grid 3 should now look like this: - -![Workflow on Grid 3][30] - -Workflow on Grid 3 - -### Execute orders - -Since you cannot buy twice, you must keep a persistent variable between the cycles that indicates whether you have already bought. - -You can do this with a **Stack element**. The Stack element is, as the name suggests, a representation of a file-based stack that can be filled with any Python data type. - -You need to define that the stack contains only one Boolean element, which determines if you bought (**True**) or not (**False**). As a consequence, you have to preset the stack with one **False**. You can set this up, for example, in Grid 4 by simply passing a **False** to the stack. - -![Forward a False-variable to the subsequent Stack element][31] - -Forward a **False** variable to the subsequent Stack element - -The Stack instances after the branch tree can be configured as follows: - -![Configuration of the Stack element][32] - -Configuring the Stack element - -In the Stack element configuration, set **Do this with input** to **Nothing**. Otherwise, the Boolean value will be overwritten by a 1 or 0. - -This configuration ensures that only one value is ever saved in the stack (**True** or **False**), and only one value can ever be read (for clarity). - -Right after the Stack element, you need an additional **Branch** element to evaluate the stack value before you place the **Binance Order** elements. - -![Evaluate the variable from the stack][33] - -Evaluating the variable from the stack - -Append the Binance Order element to the **True** path of the Branch element. The workflow on Grid 3 should now look like this: - -![Workflow on Grid 3][34] - -Workflow on Grid 3 - -The Binance Order element is configured as follows: - -![Configuration of the Binance Order element][35] - -Configuring the Binance Order element - -You can generate the API and Secret keys on the Binance website under your account settings. - -![Creating an API key in Binance][36] - -Creating an API key in the Binance account settings - -In this tutorial, every trade is executed as a market trade and has a volume of 10,000 TRX (~US$ 150 on March 2020). (For the purposes of this tutorial, I am demonstrating the overall process by using a Market Order. Because of that, I recommend using at least a Limit order.) - -The subsequent element is not triggered if the order was not executed properly (e.g., a connection issue, insufficient funds, or incorrect currency pair). Therefore, you can assume that if the subsequent element is triggered, the order was placed. - -Here is an example of output from a successful sell order for XMRBTC: - -![Output of a successfully placed sell order][37] - -Successful sell order output - -This behavior makes subsequent steps more comfortable: You can always assume that as long the output is proper, the order was placed. Therefore, you can append a **Basic Operation** element that simply writes the output to **True** and writes this value on the stack to indicate whether the order was placed or not. - -If something went wrong, you can find the details in the logging message (if logging is enabled). - -![Logging output of Binance Order element][38] - -Logging output from Binance Order element - -### Schedule and sync - -For regular scheduling and synchronization, prepend the entire workflow in Grid 1 with the **Binance Scheduler** element. - -![Binance Scheduler at Grid 1, Position 1A][39] - -Binance Scheduler at Grid 1, Position 1A - -The Binance Scheduler element executes only once, so split the execution path on the end of Grid 1 and force it to re-synchronize itself by passing the output back to the Binance Scheduler element. - -![Grid 1: Split execution path][40] - -Grid 1: Split execution path - -Element 5A points to Element 1A of Grid 2, and Element 5B points to Element 1A of Grid 1 (Binance Scheduler). - -### Deploy - -You can run the whole setup 24/7 on your local machine, or you could host it entirely on an inexpensive cloud system. For example, you can use a Linux/FreeBSD cloud system for about US$5 per month, but they usually don't provide a window system. If you want to take advantage of these low-cost clouds, you can use PythonicDaemon, which runs completely inside the terminal. - -![PythonicDaemon console interface][41] - -PythonicDaemon console - -PythonicDaemon is part of the basic installation. To use it, save your complete workflow, transfer it to the remote running system (e.g., by Secure Copy [SCP]), and start PythonicDaemon with the workflow file as an argument: - - -``` -`$ PythonicDaemon trading_bot_one` -``` - -To automatically start PythonicDaemon at system startup, you can add an entry to the crontab: - - -``` -`# crontab -e` -``` - -![Crontab on Ubuntu Server][42] - -Crontab on Ubuntu Server - -### Next steps - -As I wrote at the beginning, this tutorial is just a starting point into automated trading. Programming trading bots is approximately 10% programming and 90% testing. When it comes to letting your bot trade with your money, you will definitely think thrice about the code you program. So I advise you to keep your code as simple and easy to understand as you can. - -If you want to continue developing your trading bot on your own, the next things to set up are: - - * Automatic profit calculation (hopefully only positive!) - * Calculation of the prices you want to buy for - * Comparison with your order book (i.e., was the order filled completely?) - - - -You can download the whole example on [GitHub][2]. - --------------------------------------------------------------------------------- - -via: https://opensource.com/article/20/4/python-crypto-trading-bot - -作者：[Stephan Avenwedde][a] -选题：[lujun9972][b] -译者：[译者ID](https://github.com/译者ID) -校对：[校对者ID](https://github.com/校对者ID) - -本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 - -[a]: https://opensource.com/users/hansic99 -[b]: https://github.com/lujun9972 -[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/calculator_money_currency_financial_tool.jpg?itok=2QMa1y8c (scientific calculator) -[2]: https://github.com/hANSIc99/Pythonic -[3]: https://opensource.com/article/19/5/graphically-programming-pythonic -[4]: https://tron.network/ -[5]: https://bitcoin.org/en/ -[6]: https://www.binance.com/ -[7]: https://www.investopedia.com/terms/e/ema.asp -[8]: https://opensource.com/sites/default/files/uploads/1_ema-25.png (TRX/BTC 1-hour candle chart) -[9]: https://en.wikipedia.org/wiki/Open-high-low-close_chart -[10]: https://opensource.com/sites/default/files/uploads/2_data-mining-workflow.png (Data-mining workflow) -[11]: https://opensource.com/sites/default/files/uploads/3_ohlc-query.png (Configuration of the OHLC query element) -[12]: https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe -[13]: https://opensource.com/sites/default/files/uploads/4_edit-basic-operation.png (Basic Operation element set up to use Vim) -[14]: https://opensource.com/sites/default/files/uploads/6_grid2-workflow.png (Technical analysis workflow in Grid 2) -[15]: https://opensource.com/sites/default/files/uploads/7_technical-analysis-config.png (Configuration of the technical analysis element) -[16]: https://opensource.com/sites/default/files/uploads/8_missing-decimals.png (Missing decimal places in output) -[17]: https://opensource.com/sites/default/files/uploads/9_basic-operation-element.png (Workflow in Grid 2) -[18]: https://opensource.com/sites/default/files/uploads/10_dump-extended-dataframe.png (Dump extended DataFrame to file) -[19]: https://opensource.com/sites/default/files/uploads/11_load-dataframe-decimals.png (Representation with all decimal places) -[20]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html -[21]: https://opensource.com/sites/default/files/uploads/12_trade-factor-decision.png (Buy/sell decision) -[22]: https://opensource.com/sites/default/files/uploads/13_validation-function.png (Validation function) -[23]: https://opensource.com/sites/default/files/uploads/14_brute-force-tf.png (Nested for loops for determining the buy and sell factor) -[24]: https://opensource.com/sites/default/files/uploads/15_system-utilization.png (System utilization while brute forcing) -[25]: https://opensource.com/sites/default/files/uploads/16_sort-profit.png (Sort profit with related trading factors in descending order) -[26]: https://opensource.com/sites/default/files/uploads/17_sorted-trading-factors.png (Sorted list of trading factors and profit) -[27]: https://opensource.com/sites/default/files/uploads/18_implemented-evaluation-logic.png (Implemented evaluation logic) -[28]: https://opensource.com/sites/default/files/uploads/19_output.png (Branch element: Grid 3 Position 2A) -[29]: https://opensource.com/sites/default/files/uploads/20_editbranch.png (Branch element: Grid 3 Position 3B) -[30]: https://opensource.com/sites/default/files/uploads/21_grid3-workflow.png (Workflow on Grid 3) -[31]: https://opensource.com/sites/default/files/uploads/22_pass-false-to-stack.png (Forward a False-variable to the subsequent Stack element) -[32]: https://opensource.com/sites/default/files/uploads/23_stack-config.png (Configuration of the Stack element) -[33]: https://opensource.com/sites/default/files/uploads/24_evaluate-stack-value.png (Evaluate the variable from the stack) -[34]: https://opensource.com/sites/default/files/uploads/25_grid3-workflow.png (Workflow on Grid 3) -[35]: https://opensource.com/sites/default/files/uploads/26_binance-order.png (Configuration of the Binance Order element) -[36]: https://opensource.com/sites/default/files/uploads/27_api-key-binance.png (Creating an API key in Binance) -[37]: https://opensource.com/sites/default/files/uploads/28_sell-order.png (Output of a successfully placed sell order) -[38]: https://opensource.com/sites/default/files/uploads/29_binance-order-output.png (Logging output of Binance Order element) -[39]: https://opensource.com/sites/default/files/uploads/30_binance-scheduler.png (Binance Scheduler at Grid 1, Position 1A) -[40]: https://opensource.com/sites/default/files/uploads/31_split-execution-path.png (Grid 1: Split execution path) -[41]: https://opensource.com/sites/default/files/uploads/32_pythonic-daemon.png (PythonicDaemon console interface) -[42]: https://opensource.com/sites/default/files/uploads/33_crontab.png (Crontab on Ubuntu Server) diff --git a/translated/tech/20200415 How to automate your cryptocurrency trades with Python.md b/translated/tech/20200415 How to automate your cryptocurrency trades with Python.md new file mode 100644 index 0000000000..85533f8438 --- /dev/null +++ b/translated/tech/20200415 How to automate your cryptocurrency trades with Python.md @@ -0,0 +1,429 @@ +[#]: collector: (lujun9972) +[#]: translator: (wyxplus) +[#]: reviewer: ( ) +[#]: publisher: ( ) +[#]: url: ( ) +[#]: subject: (How to automate your cryptocurrency trades with Python) +[#]: via: (https://opensource.com/article/20/4/python-crypto-trading-bot) +[#]: author: (Stephan Avenwedde https://opensource.com/users/hansic99) + + +如何使用 Python 来自动交易加密货币 +====== + +在本教程中，教你如何设置和使用 Pythonic 来编程。它是一个图形化编程工具，用户可以很容易地使用现成的函数模块创建 Python 程序。 + +![scientific calculator][1] + +然而，不像纽约证券交易所这样的传统证券交易所一样，有一段固定的交易时间。对于加密货币而言，则是 7×24 小时交易，任何人都无法独自盯着市场。 + +在以前，我经常思考与加密货币交易相关的问题： + +- 一夜之间发生了什么？ +- 为什么没有日志记录？ +- 为什么下单？ +- 为什么不下单？ + +通常的解决手段是当在你做其他事情时，例如睡觉、与家人在一起或享受空闲时光，使用加密交易机器人代替你下单。虽然有很多商业解决方案可用，但是我选择开源的解决方案，因此我编写了加密交易机器人 [Pythonic][2]。 正如去年 [我写过的文章][3] 一样，“ Pythonic 是一种图形化编程工具，它让用户可以轻松使用现成的功能模块来创建Python应用程序。” 最初它是作为加密货币机器人使用，并具有可扩展的日志记录引擎以及经过精心测试的可重用部件，例如调度器和计时器。 + +### 开始 + +本教程将教你如何开始使用 Pythonic 进行自动交易。我选择 [币安][6]Binance[币安][6]Binance 交易所的 [波场][4]Tron[波场][4]Tron 与 [比特币][3]Bitcoin[比特币][3]Bitcoin + +交易对为例。我之所以选择这些加密货币，是因为它们彼此之间的波动性大，而不是出于个人喜好。 + +机器人将根据 [指数移动平均][7] （EMAs）来做出决策。 + +![TRX/BTC 1-hour candle chart][8] + +TRX/BTC 1 小时 K 线图 + +EMA 指标通常是指加权移动平均线，可以对近期价格数据赋予更多权重。尽管移动平均线可能只是一个简单的指标，但我能熟练使用它。 + +上图中的紫色线显示了 EMA-25 指标（这表示要考虑最近的 25 个值）。 + +机器人监视当前的 EMA-25 值（t0）和前一个 EMA-25 值（t-1）之间的差距。如果差值超过某个值，则表示价格上涨，机器人将下达购买订单。如果差值低于某个值，则机器人将下达卖单。 + +差值将是做出交易决策的主要指标。在本教程中，它称为交易参数。 + +### 工具链 + + + +将在本教程使用如下工具： + +- 币安专业交易视图（已经有其他人做了数据可视化，所以不需要重复造轮子） +- Jupyter Notebook：用于数据科学任务 +- Pythonic：作为整体框架 +- PythonicDaemon ：作为终端运行（仅适用于控制台和 Linux） + + + +### 数据挖掘 + +为了使加密货币交易机器人尽可能能做出正确的决定，以可靠的方式获取资产的美国线（[OHLC][9]）数据是至关重要。你可以使用 Pythonic 的内置元素，还可以根据自己逻辑来对其进行扩展。 + +一般的工作流程： + +1. 与币安时间同步 +2. 下载 OHLC 数据 +3. 从文件中把 OHLC 数据加载到内存 +4. 比较数据集并扩展更新数据集 + + + +这个工作流程可能有点夸张，但是它能使得程序更加健壮，甚至在停机和断开连接时，也能平稳运行。 + +一开始，你需要 **币安 OHLC 查询**Binance OHLC Query**币安 OHLC 查询**Binance OHLC Query 元素和一个 **基础操作**Basic Operation**基础操作**Basic Operation 元素来执行你的代码。 + +![Data-mining workflow][10] + +数据挖掘工作流程 + +OHLC 查询设置为每隔一小时查询一次 **TRXBTC** 资产对（波场/比特币）。 + +![Configuration of the OHLC query element][11] + +配置 OHLC 查询元素 + +其中输出的元素是 [Pandas DataFrame][12]。你可以在 **基础操作** 元素中使用 **输入**input**输入**input 变量来访问 DataFrame。其中，将 Vim 设置为 **基础操作** 元素的默认代码编辑器。 + +![Basic Operation element set up to use Vim][13] + +使用 Vim 编辑基础操作元素 + +具体代码如下： + + +``` +import pickle, pathlib, os +import pandas as pd + +outout = None + +if isinstance(input, pd.DataFrame): + file_name = 'TRXBTC_1h.bin' + home_path = str(pathlib.Path.home()) + data_path = os.path.join(home_path, file_name) + + try: + df = pickle.load(open(data_path, 'rb')) + n_row_cnt = df.shape[0] + df = pd.concat([df,input], ignore_index=True).drop_duplicates(['close_time']) + df.reset_index(drop=True, inplace=True) + n_new_rows = df.shape[0] - n_row_cnt + log_txt = '{}: {} new rows written'.format(file_name, n_new_rows) + except: + log_txt = 'File error - writing new one: {}'.format(e) + df = input + + pickle.dump(df, open(data_path, "wb" )) + output = df +``` + +首先，检查输入是否为 DataFrame 元素。然后在用户的家目录（**〜/ **）中查找名为 **TRXBTC_1h.bin** 的文件。如果存在，则将其打开，执行新代码段（**try** 部分中的代码），并删除重复项。如果文件不存在，则触发异常并执行 **except** 部分中的代码，创建一个新文件。 + +只要启用了复选框 **日志输出**log output**日志输出**log output，你就可以使用命令行工具 **tail** 查看日志记录： + + +``` +`$ tail -f ~/Pythonic_2020/Feb/log_2020_02_19.txt` +``` + +出于开发目的，现在跳过与币安时间的同步和计划执行，这将在下面实现。 + +### 准备数据 + +下一步是在单独的 网格Grid网格Grid 中处理评估逻辑。因此，你必须借助 **返回元素**Return element**返回元素**Return element 将 DataFrame 从网格 1 传递到网格 2 的第一个元素。 + +在网格 2 中，通过使 DataFrame 通过 **基础技术分析**Basic Technical Analysis**基础技术分析**Basic Technical Analysis 元素，将 DataFrame 扩展包含 EMA 值的一列。 + +![Technical analysis workflow in Grid 2][14] + +在网格 2 中技术分析工作流程 + +配置技术分析元素以计算 25 个值的 EMAs。 + +![Configuration of the technical analysis element][15] + +配置技术分析元素 + +当你运行整个程序并开启 **技术分析**Technical Analysis**技术分析**Technical Analysis 元素的调试输出时，你将发现 EMA-25 列的值似乎都相同。 + +![Missing decimal places in output][16] + +输出中精度不够 + +这是因为调试输出中的 EMA-25 值仅包含六位小数，即使输出保留了 8 个字节完整精度的浮点值。 + +为了能进行进一步处理，请添加 **基础操作** 元素： + +![Workflow in Grid 2][17] + +网格 2 中的工作流程 + +使用 **基础操作** 元素，将 DataFrame 与添加的 EMA-25 列一起转储，以便可以将其加载到 Jupyter Notebook中； + +![Dump extended DataFrame to file][18] + +将扩展后的 DataFrame 存储到文件中 + +### 评估策略 + +在 Juypter Notebook 中开发评估策略，让你可以更直接地访问代码。要加载 DataFrame，你需要使用如下代码： + +![Representation with all decimal places][19] + +用全部小数位表示 + +你可以使用 [**iloc**][20] 和列名来访问最新的 EMA-25 值，并且会保留所有小数位。 + +你已经知道如何来获得最新的数据。上面示例的最后一行仅显示该值。为了能将该值拷贝到不同的变量中，你必须使用如下图所示的 **.at** 方法方能成功。 + +你也可以直接计算出你下一步所需的交易参数。 + +![Buy/sell decision][21] + +买卖决策 + +### 确定交易参数 + +如上面代码所示，我选择 0.009 作为交易参数。但是我怎么知道 0.009 是决定交易的一个好参数呢？ 实际上，这个参数确实很糟糕，因此，你可以直接计算出表现最佳的交易参数。 + +假设你将根据收盘价进行买卖。 + +![Validation function][22] + +回测功能 + +在此示例中，**buy_factor** 和 **sell_factor** 是预先定义好的。因此，发散思维用直接计算出表现最佳的参数。 + +![Nested for loops for determining the buy and sell factor][23] + +嵌套的 _for_ 循环，用于确定购买和出售的参数 + +这要跑 81 个循环（9x9），在我的机器（Core i7 267QM）上花费了几分钟。 + +![System utilization while brute forcing][24] + +在暴力运算时系统的利用率 + +在每个循环之后，它将 **buy_factor**，**sell_factor** 元组和生成的 **利润**profit**利润**profit 元组追加到 **trading_factors** 列表中。按利润降序对列表进行排序。 + +![Sort profit with related trading factors in descending order][25] + +将利润与相关的交易参数按降序排序 + +当你打印出列表时，你会看到 0.002 是最好的参数。 + +![Sorted list of trading factors and profit][26] + +交易要素和收益的有序列表 + +当我在 2020 年 3 月写下这篇文章时，价格的波动还不足以呈现出更理想的结果。我在 2 月份得到了更好的结果，但即使在那个时候，表现最好的交易参数也在 0.002 左右。 + +### 分割执行路径 + +现在开始新建一个网格以保持逻辑清晰。使用 **返回** 元素将带有 EMA-25 列的 DataFrame 从网格 2 传递到网格 3 的 0A 元素。 + +在网格 3 中，添加 **基础操作** 元素以执行评估逻辑。这是该元素中的代码： + +![Implemented evaluation logic][27] + +实现评估策略 + +如果输出 **1** 表示你应该购买，如果输出 **2** 则表示你应该卖出。 输出 **0** 表示现在无需操作。使用 **分支**Branch**分支**Branch 元素来控制执行路径。 + +![Branch element: Grid 3 Position 2A][28] + +Branch 元素：网格 3，2A 位置 + + + +因为 **0** 和 **-1** 的处理流程一样，所以你需要在最右边添加一个分支元素来判断你是否应该卖出。 + +![Branch element: Grid 3 Position 3B][29] + +分支元素：网格 3，3B 位置 + +网格 3 应该现在如下图所示： + +![Workflow on Grid 3][30] + +网格 3 的工作流程 + +### 下单 + +由于无需在一个周期中购买两次，因此必须在周期之间保留一个持久变量，以指示你是否已经购买。 + +你可以利用 **栈**Stack**栈**Stack 元素来实现。顾名思义，栈元素表示可以用任何 Python 数据类型来放入的基于文件的栈。 + +你需要定义栈仅包含一个布尔类型，该布尔类型决定是否购买了（**True**）或（**False**）。因此，你必须使用 **False** 来初始化栈。例如，你可以在网格 4 中简单地通过将 **False** 传递给栈来进行设置。![Forward a False-variable to the subsequent Stack element][31] + +将 **False** 变量传输到后续的栈元素中 + +在分支树后的栈实例可以进行如下配置： + +![Configuration of the Stack element][32] + +设置栈元素 + +在栈元素设置中，将 **Do this with input** 设置成 **Nothing**。否则，布尔值将被 1 或 0 覆盖。 + +该设置确保仅将一个值保存于栈中（**True** 或 **False**），并且只能读取一个值（为了清楚起见）。 + +在栈元素之后，你需要另外一个 **分支** 元素来判断栈的值，然后再放置 **币安订单**Binance Order**币安订单**Binance Order 元素。 + +![Evaluate the variable from the stack][33] + +判断栈中的变量 + +将币安订单元素添加到分支元素的 **True** 路径。网格 3 上的工作流现在应如下所示： + +![Workflow on Grid 3][34] + +网格 3 的工作流程 + +币安订单元素应如下配置： + +![Configuration of the Binance Order element][35] + +编辑币安订单元素 + +你可以在币安网站上的帐户设置中生成 API 和密钥。 + +![Creating an API key in Binance][36] + +在币安账户设置中创建一个 API key + +在本文中，每笔交易都是作为市价交易执行的，交易量为10,000 TRX（2020 年 3 月约为 150 美元）（出于教学的目的，我通过使用市价下单来演示整个过程。因此，我建议至少使用限价下单。） + +如果未正确执行下单（例如，网络问题、资金不足或货币对不正确），则不会触发后续元素。因此，你可以假定如果触发了后续元素，则表示该订单已下达。 + +这是一个成功的 XMRBTC 卖单的输出示例： + +![Output of a successfully placed sell order][37] + +成功卖单的输出 + +该行为使后续步骤更加简单：你可以始终假设只要成功输出，就表示订单成功。因此，你可以添加一个 **基础操作** 元素，该元素将简单地输出 **True** 并将此值放入栈中以表示是否下单。 + +如果出现错误的话，你可以在日志信息中查看具体细节（如果启用日志功能）。 + +![Logging output of Binance Order element][38] + +币安订单元素中的输出日志信息 + +### 调度和同步 + +对于日程调度和同步，请在网格 1 中将整个工作流程置于 **币安调度器**Binance Scheduler**币安调度器**Binance Scheduler 元素的前面。 + +![Binance Scheduler at Grid 1, Position 1A][39] + +在网格 1，1A 位置的币安调度器 + +由于币安调度器元素只执行一次，因此请在网格 1 的末尾拆分执行路径，并通过将输出传递回币安调度器来强制让其重新同步。 + +![Grid 1: Split execution path][40] + +网格 1：拆分执行路径 + +5A 元素指向 网格 2 的 1A 元素，并且 5B 元素指向网格 1 的 1A 元素（币安调度器）。 + +### 部署 + +你可以在本地计算机上全天候 7×24 小时运行整个程序，也可以将其完全托管在廉价的云系统上。例如，你可以使用 Linux/FreeBSD 云系统，每月约 5 美元，但通常不提供图形化界面。如果你想利用这些低成本的云，可以使用 PythonicDaemon，它能在终端中完全运行。 + +![PythonicDaemon console interface][41] + +PythonicDaemon 控制台 + +PythonicDaemon 是基础程序的一部分。要使用它，请保存完整的工作流程，将其传输到远程运行的系统中（例如，通过安全拷贝协议Secure Copy安全拷贝协议Secure Copy [SCP]），然后把工作流程文件作为参数来启动 PythonicDaemon： + + +``` +`$ PythonicDaemon trading_bot_one` +``` + +为了能在系统启动时自启 PythonicDaemon，可以将一个条目添加到 crontab 中： + + +``` +`# crontab -e` +``` + +![Crontab on Ubuntu Server][42] + +在 Ubuntu 服务器上的 Crontab + +### 下一步 + +正如我在一开始时所说的，本教程只是自动交易的入门。对交易机器人进行编程大约需要 10％ 的编程和 90％ 的测试。当涉及到让你的机器人用金钱交易时，你肯定会对编写的代码再三思考。因此，我建议你编码时要尽可能简单和易于理解。 + + + +如果你想自己继续开发交易机器人，接下来所需要做的事： + +- 收益自动计算（希望你有正收益！） +- 计算你想买的价格 +- 比较你的预订单（例如，订单是否填写完整？） + + + +你可以从 [GitHub][2] 上获取完整代码。 + +-------------------------------------------------------------------------------- + +via: https://opensource.com/article/20/4/python-crypto-trading-bot + +作者：[Stephan Avenwedde][a] +选题：[lujun9972][b] +译者：[wyxplus](https://github.com/wyxplus) +校对：[校对者ID](https://github.com/校对者ID) + +本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 + +[a]: https://opensource.com/users/hansic99 +[b]: https://github.com/lujun9972 +[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/calculator_money_currency_financial_tool.jpg?itok=2QMa1y8c "scientific calculator" +[2]: https://github.com/hANSIc99/Pythonic +[3]: https://opensource.com/article/19/5/graphically-programming-pythonic +[4]: https://tron.network/ +[5]: https://bitcoin.org/en/ +[6]: https://www.binance.com/ +[7]: https://www.investopedia.com/terms/e/ema.asp +[8]: https://opensource.com/sites/default/files/uploads/1_ema-25.png "TRX/BTC 1-hour candle chart" +[9]: https://en.wikipedia.org/wiki/Open-high-low-close_chart +[10]: https://opensource.com/sites/default/files/uploads/2_data-mining-workflow.png "Data-mining workflow" +[11]: https://opensource.com/sites/default/files/uploads/3_ohlc-query.png "Configuration of the OHLC query element" +[12]: https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe +[13]: https://opensource.com/sites/default/files/uploads/4_edit-basic-operation.png "Basic Operation element set up to use Vim" +[14]: https://opensource.com/sites/default/files/uploads/6_grid2-workflow.png "Technical analysis workflow in Grid 2" +[15]: https://opensource.com/sites/default/files/uploads/7_technical-analysis-config.png "Configuration of the technical analysis element" +[16]: https://opensource.com/sites/default/files/uploads/8_missing-decimals.png "Missing decimal places in output" +[17]: https://opensource.com/sites/default/files/uploads/9_basic-operation-element.png "Workflow in Grid 2" +[18]: https://opensource.com/sites/default/files/uploads/10_dump-extended-dataframe.png "Dump extended DataFrame to file" +[19]: https://opensource.com/sites/default/files/uploads/11_load-dataframe-decimals.png "Representation with all decimal places" +[20]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html +[21]: https://opensource.com/sites/default/files/uploads/12_trade-factor-decision.png "Buy/sell decision" +[22]: https://opensource.com/sites/default/files/uploads/13_validation-function.png "Validation function" +[23]: https://opensource.com/sites/default/files/uploads/14_brute-force-tf.png "Nested for loops for determining the buy and sell factor" +[24]: https://opensource.com/sites/default/files/uploads/15_system-utilization.png "System utilization while brute forcing" +[25]: https://opensource.com/sites/default/files/uploads/16_sort-profit.png "Sort profit with related trading factors in descending order" +[26]: https://opensource.com/sites/default/files/uploads/17_sorted-trading-factors.png "Sorted list of trading factors and profit" +[27]: https://opensource.com/sites/default/files/uploads/18_implemented-evaluation-logic.png "Implemented evaluation logic" +[28]: https://opensource.com/sites/default/files/uploads/19_output.png "Branch element: Grid 3 Position 2A" +[29]: https://opensource.com/sites/default/files/uploads/20_editbranch.png "Branch element: Grid 3 Position 3B" +[30]: https://opensource.com/sites/default/files/uploads/21_grid3-workflow.png "Workflow on Grid 3" +[31]: https://opensource.com/sites/default/files/uploads/22_pass-false-to-stack.png "Forward a False-variable to the subsequent Stack element" +[32]: https://opensource.com/sites/default/files/uploads/23_stack-config.png "Configuration of the Stack element" +[33]: https://opensource.com/sites/default/files/uploads/24_evaluate-stack-value.png "Evaluate the variable from the stack" +[34]: https://opensource.com/sites/default/files/uploads/25_grid3-workflow.png "Workflow on Grid 3" +[35]: https://opensource.com/sites/default/files/uploads/26_binance-order.png "Configuration of the Binance Order element" +[36]: https://opensource.com/sites/default/files/uploads/27_api-key-binance.png "Creating an API key in Binance" +[37]: https://opensource.com/sites/default/files/uploads/28_sell-order.png "Output of a successfully placed sell order" +[38]: https://opensource.com/sites/default/files/uploads/29_binance-order-output.png "Logging output of Binance Order element" +[39]: https://opensource.com/sites/default/files/uploads/30_binance-scheduler.png "Binance Scheduler at Grid 1, Position 1A" +[40]: https://opensource.com/sites/default/files/uploads/31_split-execution-path.png "Grid 1: Split execution path" +[41]: https://opensource.com/sites/default/files/uploads/32_pythonic-daemon.png "PythonicDaemon console interface" +[42]: https://opensource.com/sites/default/files/uploads/33_crontab.png "Crontab on Ubuntu Server"