Quick Start

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:

Installed Visual Studio Code
Installed .NET 6 SDK
Installed .NET Interactive Notebooks extension

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:

In [ ]:

#r "nuget:QuanTAlib;"
using QuanTAlib;

YAHOO_Feed aapl = new(15, "AAPL");
TSeries data = aapl.Close;
SMA_Series sma = new(source: data, period: 5, useNaN: false);
EMA_Series ema = new(sma, period: 5);                   // by default, indicators expose all data, no NaN values
WMA_Series wma = new(ema, 5, useNaN: true);             // for the final calculation we can hide early data with NaNs

Console.Write($"index\t data\t\t sma(data)\t ema(sma(data))\t wma(ema(sma(data)))\n");
for (int i=0; i<data.Count; i++)
    Console.Write($"{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");

index	 data		 sma(data)	 ema(sma(data))	 wma(ema(sma(data)))
0	 2022-03-23	 170.21		 170.21		 NaN
1	 2022-03-24	 172.14		 170.85		 NaN
2	 2022-03-25	 173.00		 171.57		 NaN
3	 2022-03-28	 173.65		 172.26		 NaN
4	 2022-03-29	 174.71		 173.08		 172.07
5	 2022-03-30	 176.22		 174.13		 172.92
6	 2022-03-31	 176.33		 174.86		 173.74
7	 2022-04-01	 176.25		 175.32		 174.46
8	 2022-04-04	 176.82		 175.82		 175.09
9	 2022-04-05	 176.04		 175.89		 175.51
10	 2022-04-06	 174.85		 175.55		 175.62
11	 2022-04-07	 174.36		 175.15		 175.51

Understanding QuanTAlib data model

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:

In [ ]:

var item1 = (DateTime.Today, 105.3);        // (DateTime, Value) tuple
double item2 = 293.1;                       // a simple double

TSeries data = new();
data.Add(item1);                            // adding tuple variable
data.Add(item2);                            // QuanTAlib stamps the (double) with current time
data.Add(0);                                // directly adding a number (stamped with current time)
data.Add((DateTime.Now.AddDays(-3), 10));   // adding a tuple with timestamp 3 days ago

data

index	Item1	Item2
0	2022-04-07 00:00:00Z	105.3
1	2022-04-07 21:57:46Z	293.1
2	2022-04-07 21:57:46Z	0
3	2022-04-04 21:57:46Z	10

TSeries list can display only values (without timestamps) or only timestamps (without values) by using .v or .t properties

In [ ]:

data.v

index	value
0	105.3
1	293.1
2	0
3	10

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

In [ ]:

bool IsTheSame = data.Last().v == data[^1].v;
double lastvalue = data;

lastvalue

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:

In [ ]:

TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher
EMA_Series t2 = new(t1, 3);     // t2 will auto-load all history of t1 and wait for events from t1
ADD_Series t3 = new(t1, t2);    // t3 is an ADDition of t1 and t2 - will also load history and wait for t2 events
DIV_Series t4 = new(1, t3);     // t4 is calculating 1/t3 - and waiting for t3 events

TSeries t5 = new();             // a wild indicator appeared! And it is empty!
t4.Pub += t5.Sub;               // let us add a manual subscription to events coming from t4 - t5 is now listening to t4
t1.Add(0);                      // we add one new value to t1 - and trigger the full cascade of calculation! t5 is now full!

t5.v

index	value
0	Infinity
1	0.6666666666666666
2	0.3076923076923077
3	0.1951219512195122
4	0.1415929203539823
5	0.11072664359861592
6	0.09078014184397164
7	0.07687687687687687
8	0.06664931007550118
9	0.05881677197013211
10	0.2499389797412741

MACD compounded indicator

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:

In [ ]:

YAHOO_Feed aapl = new(20, "AAPL");
TSeries close = aapl.Close;                 // close will get data from history
EMA_Series slow = new(close,26);            // slow gets data from slow through pub-sub eventing
EMA_Series fast = new(close,12);            // fast gets data from slow (via eventing)
SUB_Series macd = new(fast,slow);           // macd is a SUBtraction: fast-slow
EMA_Series signal = new(macd,9);            // signal is EMA of macd
SUB_Series histogram = new(macd, signal);   // histogram is SUBtraction macd-signal

histogram.v

index	value
0	0
1	0.08934530370370339
2	0.3599908358509947
3	0.5984373224068585
4	0.9604679820661939
5	1.17393637722238
6	1.295101583309171
7	1.5073770108948183
8	1.4718244887751928
9	1.1537265475126177
10	0.8550987734004014
11	0.8650928385987653
12	0.5867583008849087
13	0.15053155636913873
14	-0.13622024638714825

12 KiB Raw Blame History

Quick Start

Understanding QuanTAlib data model

MACD compounded indicator

12 KiB

Raw Blame History