mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-28 17:57:45 +00:00
Miha Kralj
34997cd0d6
Add new EQUITY_Series and updates to docs, Calculations, Indicators, Strategies, Tests, and .github/workflows
446 lines
15 KiB
Plaintext
446 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Quick Start\n",
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"\n",
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"In order to use this .NET Interactive Notebook and play along with QuanTAlib (outside of making your own app or plugging QuanTAlib into Quantower platform), you will need:\n",
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"\n",
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"- Installed <a href=\"https://code.visualstudio.com/\" target=\"_blank\">Visual Studio Code</a>\n",
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"- Installed <a href=\"https://dotnet.microsoft.com/download/dotnet/6.0\" target=\"_blank\">.NET 6 SDK</a>\n",
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"- Installed <a href=\"https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode\" target=\"_blank\">.NET Interactive Notebooks</a> extension\n",
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"\n",
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"**For impatient**, here is a simple example of calculating three moving averages - SMA(data), EMA(SMA(data)) and WMA(EMA(SMA(data))) from 10 days of AAPL stock data using QuanTAlib:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"dotnet_interactive": {
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"language": "csharp"
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div><div></div><div></div><div></div></div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"index\t data\t\t sma(data)\t ema(sma(data))\t wma(ema(sma(data)))\n",
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"0\t 2023-03-27\t 158.28\t\t 158.28\t\t NaN\n",
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"1\t 2023-03-28\t 157.97\t\t 158.12\t\t NaN\n",
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"2\t 2023-03-29\t 158.90\t\t 158.38\t\t NaN\n",
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"3\t 2023-03-30\t 159.77\t\t 158.73\t\t NaN\n",
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"4\t 2023-03-31\t 160.79\t\t 159.14\t\t 158.69\n",
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"5\t 2023-04-03\t 162.37\t\t 160.22\t\t 159.25\n",
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"6\t 2023-04-04\t 163.97\t\t 161.47\t\t 160.10\n",
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"7\t 2023-04-05\t 164.56\t\t 162.50\t\t 161.07\n",
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"8\t 2023-04-06\t 165.02\t\t 163.34\t\t 162.04\n"
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]
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}
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],
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"source": [
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"#r \"nuget:QuanTAlib;\"\n",
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"using QuanTAlib;\n",
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"\n",
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"Yahoo_Feed aapl = new(\"AAPL\", 10);\n",
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"TSeries data = aapl.Close;\n",
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"SMA_Series sma = new(source: data, period: 5, useNaN: false);\n",
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"EMA_Series ema = new(sma, period: 5); // by default, indicators expose all data, no NaN values\n",
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"WMA_Series wma = new(ema, 5, useNaN: true); // for the final calculation we can hide early data with NaNs\n",
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"\n",
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"Console.Write($\"index\\t data\\t\\t sma(data)\\t ema(sma(data))\\t wma(ema(sma(data)))\\n\");\n",
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"for (int i=0; i<aapl.Count; i++)\n",
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" Console.Write($\"{i}\\t {data[i].t:yyyy-MM-dd}\\t {sma[i].v:f2}\\t\\t {ema[i].v:f2}\\t\\t {wma[i].v:f2}\\n\");"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Understanding QuanTAlib data model\n",
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"\n",
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"QuanTAlib expects that every data item is a tuple (TimeDate t, double v) and TSeries is a list of (t,v) tuples. There are several helpers built into the TSeries class to simplify adding elements:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"dotnet_interactive": {
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"language": "csharp"
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"outputs": [
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"<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><details class=\"dni-treeview\"><summary><span class=\"dni-code-hint\"><code>(4/7/2023 12:00:00 AM, 105.3)</code></span></summary><div><table><thead><tr></tr></thead><tbody><tr><td>Item1</td><td><span>2023-04-07 00:00:00Z</span></td></tr><tr><td>Item2</td><td><div class=\"dni-plaintext\"><pre>105.3</pre></div></td></tr></tbody></table></div></details></td></tr><tr><td>1</td><td><details class=\"dni-treeview\"><summary><span class=\"dni-code-hint\"><code>(4/7/2023 2:34:48 PM, 293.1)</code></span></summary><div><table><thead><tr></tr></thead><tbody><tr><td>Item1</td><td><span>2023-04-07 14:34:48Z</span></td></tr><tr><td>Item2</td><td><div class=\"dni-plaintext\"><pre>293.1</pre></div></td></tr></tbody></table></div></details></td></tr><tr><td>2</td><td><details class=\"dni-treeview\"><summary><span class=\"dni-code-hint\"><code>(4/7/2023 2:34:48 PM, 0)</code></span></summary><div><table><thead><tr></tr></thead><tbody><tr><td>Item1</td><td><span>2023-04-07 14:34:48Z</span></td></tr><tr><td>Item2</td><td><div class=\"dni-plaintext\"><pre>0</pre></div></td></tr></tbody></table></div></details></td></tr><tr><td>3</td><td><details class=\"dni-treeview\"><summary><span class=\"dni-code-hint\"><code>(4/4/2023 2:34:48 PM, 10)</code></span></summary><div><table><thead><tr></tr></thead><tbody><tr><td>Item1</td><td><span>2023-04-04 14:34:48Z</span></td></tr><tr><td>Item2</td><td><div class=\"dni-plaintext\"><pre>10</pre></div></td></tr></tbody></table></div></details></td></tr></tbody></table><style>\r\n",
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"details.dni-treeview {\r\n",
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"}\r\n",
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"table td {\r\n",
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"}\r\n",
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"table tr { \r\n",
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" vertical-align: top; \r\n",
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" margin: 0em 0px;\r\n",
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"}\r\n",
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"table tr td pre \r\n",
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"{ \r\n",
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" vertical-align: top !important; \r\n",
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"} \r\n",
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"var item1 = (DateTime.Today, 105.3); // (DateTime, Value) tuple\n",
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"double item2 = 293.1; // a simple double\n",
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"\n",
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"TSeries data = new();\n",
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"data.Add(item1); // adding tuple variable\n",
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"data.Add(item2); // QuanTAlib stamps the (double) with current time\n",
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"data.Add(0); // directly adding a number (stamped with current time)\n",
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"data.Add((DateTime.Now.AddDays(-3), 10)); // adding a tuple with timestamp 3 days ago\n",
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"\n",
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"data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties"
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]
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},
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{
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"<div class=\"dni-plaintext\"><pre>[ 105.3, 293.1, 0, 10 ]</pre></div><style>\r\n",
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"metadata": {},
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"output_type": "display_data"
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],
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"source": [
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"data.v"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The last element on the list can be accessed by .Last() or by [^1] - and using `.t` (time) and `.v` (value) properties. Also, casting a TSeries into (double) will return the value of the last element"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"dotnet_interactive": {
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"language": "csharp"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div class=\"dni-plaintext\"><pre>10</pre></div><style>\r\n",
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"bool IsTheSame = data.Last().v == data[^1].v;\n",
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"double lastvalue = data;\n",
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"\n",
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"lastvalue"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"All indicators are just modified TSeries classes; they get all required input during class construction (source of the datafeed, period...) and they automatically subscribe to events of the datafeed. Whenever datafeed gets a new value, indicator will calculate its own value. Indicators are also event publishers, so other indicators can subscribe to their results, chaining indicators together:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"dotnet_interactive": {
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"language": "csharp"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div class=\"dni-plaintext\"><pre>[ Infinity, 0.6666666666666666, 0.3333333333333333, 0.2, 0.14285714285714285, 0.1111111111111111, 0.09090909090909091, 0.07692307692307693, 0.06666666666666667, 0.058823529411764705, 0.25 ]</pre></div><style>\r\n",
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" padding-left: 1em;\r\n",
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"}\r\n",
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"table td {\r\n",
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" text-align: start;\r\n",
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"}\r\n",
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"table tr { \r\n",
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" vertical-align: top; \r\n",
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" margin: 0em 0px;\r\n",
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"}\r\n",
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"table tr td pre \r\n",
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" vertical-align: top !important; \r\n",
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"} \r\n",
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n",
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"EMA_Series t2 = new(t1, 3); // t2 will auto-load all history of t1 and wait for events from t1\n",
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"ADD_Series t3 = new(t1, t2); // t3 is an ADDition of t1 and t2 - will also load history and wait for t2 events\n",
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"DIV_Series t4 = new(1, t3); // t4 is calculating 1/t3 - and waiting for t3 events\n",
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"\n",
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"TSeries t5 = new(); // a wild indicator appeared! And it is empty!\n",
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"t4.Pub += t5.Sub; // let us add a manual subscription to events coming from t4 - t5 is now listening to t4\n",
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"t1.Add(0); // we add one new value to t1 - and trigger the full cascade of calculation! t5 is now full!\n",
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"\n",
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"t5.v"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# MACD compounded indicator\n",
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"\n",
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"With QuanTAlib we can chain indicators together, creating complex compounded indicators. For example, we can create Moving Average Convergence/Divergence (MACD) indicators by chaining all required operations in a sequence:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"dotnet_interactive": {
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"language": "csharp"
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}
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"Yahoo_Feed aapl = new(\"AAPL\", 100);\n",
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"TSeries close = aapl.Close; // close will get data from history\n",
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"EMA_Series slow = new(close,26); // slow gets data from slow through pub-sub eventing\n",
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"EMA_Series fast = new(close,12); // fast gets data from slow (via eventing)\n",
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"SUB_Series macd = new(fast,slow); // macd is a SUBtraction: fast-slow\n",
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"EMA_Series signal = new(macd,9); // signal is EMA of macd\n",
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"SUB_Series histogram = new(macd, signal); // histogram is SUBtraction macd-signal\n",
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"\n",
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"histogram.v\n"
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]
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}
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],
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"metadata": {
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