refactoring

2026-08-16 09:38:05 +00:00 · 2022-11-26 22:04:26 -08:00
parent 570a7896df
commit 58694a9600
22 changed files with 1397 additions and 1186 deletions
@@ -0,0 +1,40 @@
+# SMA: Simple Moving Average
+SMA is one of the most basic trend-following indicators used in Technical Analysis. It is calculated as the *unweighted mean* of the previous $p$ (period) data-points.
+
+
+## Calculation
+
+SMA is a rolling calculation looking backwards from the position ${n}$ and is denoted as ${SMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points:
+$$
+SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i
+$$
+When calculating the value of next $SMA_{p,next}$ while knowing all previous SMA values, SMA calculation can be reduced to:
+$$
+SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right)
+$$
+
+## Implementation
+
+``` csharp
+SMA_Series mean = new(source: data, period: p, useNaN: false);
+
+QuanTA fluent = data.SMA(period: p);
+```
+
+## Parameters
+
+- `TSeries source` -  List of value tuples (DateTime, double)
+- `int period` - Integer representing the period of SMA
+- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
+
+## Sample chart
+
+picture of SMA
+
+## Comparison & Validation
+
+Validation tests
+Performance tests
+
+## References
+   - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
@@ -2,7 +2,14 @@
 "cells": [
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "# Quick Start\n",
    "\n",
@@ -17,35 +24,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><div></div><div></div><div></div></div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "Error",
-     "evalue": "(3,1): error CS0246: The type or namespace name 'Yahoo_Feed' could not be found (are you missing a using directive or an assembly reference?)\r\n(10,15): error CS0019: Operator '<' cannot be applied to operands of type 'int' and 'method group'",
-     "output_type": "error",
-     "traceback": [
-      "(3,1): error CS0246: The type or namespace name 'Yahoo_Feed' could not be found (are you missing a using directive or an assembly reference?)\r\n",
-      "(10,15): error CS0019: Operator '<' cannot be applied to operands of type 'int' and 'method group'"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "#r \"nuget:QuanTAlib;\"\n",
    "using QuanTAlib;\n",
@@ -63,7 +51,14 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "## Understanding QuanTAlib data model\n",
    "\n",
@@ -72,26 +67,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>Item1</th><th>Item2</th></tr></thead><tbody><tr><td>0</td><td><span>2022-11-10 00:00:00Z</span></td><td><div class=\"dni-plaintext\">105.3</div></td></tr><tr><td>1</td><td><span>2022-11-10 15:47:46Z</span></td><td><div class=\"dni-plaintext\">293.1</div></td></tr><tr><td>2</td><td><span>2022-11-10 15:47:46Z</span></td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><span>2022-11-07 15:47:46Z</span></td><td><div class=\"dni-plaintext\">10</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "var item1 = (DateTime.Today, 105.3);        // (DateTime, Value) tuple\n",
    "double item2 = 293.1;                       // a simple double\n",
@@ -107,66 +92,60 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">105.3</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">293.1</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">10</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "data.v"
   ]
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"dni-plaintext\">10</div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "bool IsTheSame = data.Last().v == data[^1].v;\n",
    "double lastvalue = data;\n",
@@ -176,33 +155,30 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "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:"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">Infinity</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">0.6666666666666666</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0.3333333333333333</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">0.2</div></td></tr><tr><td>4</td><td><div class=\"dni-plaintext\">0.14285714285714285</div></td></tr><tr><td>5</td><td><div class=\"dni-plaintext\">0.1111111111111111</div></td></tr><tr><td>6</td><td><div class=\"dni-plaintext\">0.09090909090909091</div></td></tr><tr><td>7</td><td><div class=\"dni-plaintext\">0.07692307692307693</div></td></tr><tr><td>8</td><td><div class=\"dni-plaintext\">0.06666666666666667</div></td></tr><tr><td>9</td><td><div class=\"dni-plaintext\">0.058823529411764705</div></td></tr><tr><td>10</td><td><div class=\"dni-plaintext\">0.25</div></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n",
    "EMA_Series t2 = new(t1, 3);     // t2 will auto-load all history of t1 and wait for events from t1\n",
@@ -218,7 +194,14 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
   "source": [
    "# MACD compounded indicator\n",
    "\n",
@@ -227,26 +210,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "csharp"
    },
    "vscode": {
-     "languageId": "dotnet-interactive.csharp"
+     "languageId": "polyglot-notebook"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>4</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>5</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>6</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>7</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>8</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>9</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>10</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>11</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>12</td><td><div class=\"dni-plaintext\">0.13543589743590018</div></td></tr><tr><td>13</td><td><div class=\"dni-plaintext\">-0.03897954353340993</div></td></tr><tr><td>14</td><td><div class=\"dni-plaintext\">-0.17731008431411102</div></td></tr><tr><td>15</td><td><div class=\"dni-plaintext\">-0.24030671152304095</div></td></tr><tr><td>16</td><td><div class=\"dni-plaintext\">-0.08247055673614988</div></td></tr><tr><td>17</td><td><div class=\"dni-plaintext\">-0.47898448490240814</div></td></tr><tr><td>18</td><td><div class=\"dni-plaintext\">-0.9020715041856615</div></td></tr><tr><td>19</td><td><div class=\"dni-plaintext\">-1.3489730137363423</div></td></tr><tr><td colspan=\"2\"><i>(51 more)</i></td></tr></tbody></table>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "Yahoo_Feed aapl = new(\"AAPL\", 100);\n",
    "TSeries close = aapl.Close;                 // close will get data from history\n",
@@ -266,14 +239,33 @@
   "language": "C#",
   "name": ".net-csharp"
  },
-  "language_info": {
-   "file_extension": ".cs",
-   "mimetype": "text/x-csharp",
-   "name": "C#",
-   "pygments_lexer": "csharp",
-   "version": "9.0"
-  },
-  "orig_nbformat": 4
+  "polyglot_notebook": {
+   "kernelInfo": {
+    "defaultKernelName": "csharp",
+    "items": [
+     {
+      "aliases": [
+       "c#",
+       "C#"
+      ],
+      "languageName": "C#",
+      "name": "csharp"
+     },
+     {
+      "aliases": [
+       "frontend"
+      ],
+      "languageName": null,
+      "name": "vscode"
+     },
+     {
+      "aliases": [],
+      "languageName": "KQL",
+      "name": "kql"
+     }
+    ]
+   }
+  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
@@ -6,20 +6,26 @@
  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
  <meta name="description" content="Description">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, minimum-scale=1.0">
-  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsify-themeable@0/dist/css/theme-simple.css">
+  <link rel="stylesheet" href="//cdn.jsdelivr.net/npm/docsify@4/lib/themes/vue.css">
+
 </head>
 <body>
  <div id="app"></div>
  <script>
    window.$docsify = {
-      name: 'QuanTAlib',
-      repo: 'mihakralj/quantalib'
-    }
+      name: '',
+      repo: '',
+      latex: {
+        inlineMath   : [['$', '$'], ['\\(', '\\)']], // default
+        displayMath  : [['$$', '$$']],               // default
+        }
+    };
  </script>
-  <script src="//cdn.jsdelivr.net/npm/prismjs@1/components/prism-csharp.min.js"></script>
  <!-- Docsify v4 -->
  <script src="//cdn.jsdelivr.net/npm/docsify@4"></script>
-  <script src="//cdn.jsdelivr.net/npm/docsify-themeable@0/dist/js/docsify-themeable.min.js"></script>
-  <script src="//unpkg.com/@rakutentech/docsify-code-inline/dist/index.min.js"></script>
+  <!-- LaTeX display engine -->
+  <script src="//cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--  docsify-latex plugin -->
+  <script src="//cdn.jsdelivr.net/npm/docsify-latex@0"></script>
 </body>
 </html>
@@ -34,28 +34,31 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett

 ⛔= Not implemented (yet)

-| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
-|--|:--:|:--:|:--:|:--:|
+| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** | **Tulip** |
+|--|:--:|:--:|:--:|:--:|:--:|
 | ⭐ OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
 | ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 | hl2 |
 | ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 | hlc3 |
 | ⭐ OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
-| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 |
+| ⭐ OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 | avgprice |
 | ⭐ HLCC4 - Weighted Price |  `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
 | ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || midpoint |
 | ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || midprice |
-| ⭐ MAX - Max value | `MAX_Series` | MAX |||
-| ⭐ MIN - Min value | `MIN_Series` | MIN |||
-| ⭐ SUM - Summation | `SUM_Series` | SUM |||
-| ⭐ ADD - Addition | `ADD_Series` | ADD |||
-| ⭐ SUB - Subtraction | `SUB_Series` | SUB |||
-| ⭐ MUL - Multiplication | `MUL_Series` | MUL |||
-| ⭐ DIV - Division | `DIV_Series` | DIV |||
+| ⭐ MAX - Max value | `MAX_Series` | MAX ||| max |
+| ⭐ MIN - Min value | `MIN_Series` | MIN ||| min |
+| ⭐ SUM - Summation | `SUM_Series` | SUM ||| sum |
+| ⭐ ADD - Addition | `ADD_Series` | ADD ||| add |
+| ⭐ SUB - Subtraction | `SUB_Series` | SUB ||| sub |
+| ⭐ MUL - Multiplication | `MUL_Series` | MUL ||| mul |
+| ⭐ DIV - Division | `DIV_Series` | DIV ||| div |
 |||||
-| **STATISTICS & NUMERICAL ANALYSIS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **STATISTICS & NUMERICAL ANALYSIS** |
+||||||
 | ⭐ BIAS - Bias | `BIAS_Series` ||| bias |
 | ⭐ CORR - Pearson's Correlation Coefficient | `CORR_Series` | CORREL | GetCorrelation ||
 | ⭐ COVAR - Covariance | `COVAR_Series` || GetCorrelation ||
+| ⛔ DECAY - Linear Decay ||||| decay |
+| ⛔ EDECAY - Exponential Decay ||||| edecay |
 | ⭐ ENTROPY - Entropy | `ENTROPY_Series` ||| entropy |
 | ⭐ KURTOSIS - Kurtosis | `KURT_Series` ||| kurtosis |
 | ⭐ LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
@@ -73,22 +76,23 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
 | ⭐ ZSCORE - Number of standard deviations from mean | `ZSCORE_Series` || GetStdDev | zscore |
 ||||||
-| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **TREND INDICATORS & AVERAGES** |
+||||||
 | ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
 | ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma | alma |
 | ⛔ ARIMA - Autoregressive Integrated Moving Average |||||
-| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema |
-| ⭐ EMA - Exponential Moving Average | `EMA_Series` | EMA | GetEma | ema |
+| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema | dema |
+| ⭐ EMA - Exponential Moving Average | `EMA_Series` | EMA | GetEma | ema | ema |
 | ⛔ EPMA - Endpoint Moving Average ||| GetEpma ||
 | ⛔ FRAMA - Fractal Adaptive Moving Average |||||
 | ⛔ FWMA - Fibonacci's Weighted Moving Average |||| fwma |
 | ⛔ HILO - Gann High-Low Activator |||| hilo |
 | ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||||
 | ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline ||
-| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma | hma |
+| ⭐ HMA - Hull  Moving Average | `HMA_Series` || GetHma | hma | hma |
 | ⛔ HWMA - Holt-Winter Moving Average |||| hwma |
 | ✔️ JMA - Jurik Moving Average | `JMA_Series` ||| jma |
-| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama |
+| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama | kama |
 | ⛔ KDJ - KDJ Indicator (trend reversal) |||| kdj |
 | ⛔ LSMA - Least Squares Moving Average |||||
 | ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd | macd |
@@ -113,17 +117,20 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⭐ WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | wma |
 | ⭐ ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| zlma |
 ||||||
-| **VOLATILITY INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
-| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad |
-| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc |
-| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr |
+| **VOLATILITY INDICATORS** |
+||||||
+| ⭐ ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad | ad |
+| ⭐ ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc | adosc |
+| ⭐ ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr | atr |
 | ⭐ ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
 | ⛔ BETA - Beta coefficient || BETA | GetBeta ||
-| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands ||
+| ⭐ BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands || bbands |
 | ⛔ CHAND - Chandelier Exit ||| GetChandelier ||
 | ⛔ CRSI - Connor RSI ||| GetConnorsRsi ||
+| ⛔ CVI - Chaikins Volatility ||||| cvi |
 | ⛔ DON - Donchian Channels ||| GetDonchian ||
 | ⛔ FCB - Fractal Chaos Bands ||| GetFcb ||
+| ⛔ FISHER - Fisher Transform ||| GetFcb || fisher |
 | ⛔ HV - Historical Volatility |||||
 | ⛔ ICH - Ichimoku ||| GetIchimoku ||
 | ⛔ KEL - Keltner Channels ||| GetKeltner ||
@@ -137,23 +144,25 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ UI - Ulcer Index |||||
 | ⛔ VSTOP - Volatility Stop |||||
 ||||||
-| **MOMENTUM INDICATORS & OSCILLATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **MOMENTUM INDICATORS & OSCILLATORS** |
+||||||
 | ⛔ AC - Acceleration Oscillator |||||
-| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx ||
-| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx ||
-| ⛔ AO - Awesome Oscillator ||| GetAwesome ||
-| ⛔ APO - Absolute Price Oscillator || APO |||
-| ⛔ AROON - Aroon oscillator || AROON | GetAroon ||
-| ⛔ BOP - Balance of Power || BOP | GetBop ||
-| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci ||
+| ⛔ ADX - Average Directional Movement Index || ADX | GetAdx || adx |
+| ⛔ ADXR - Average Directional Movement Index Rating || ADXR | GetAdx || adxr |
+| ⛔ AO - Awesome Oscillator ||| GetAwesome || ao |
+| ⛔ APO - Absolute Price Oscillator || APO ||| apo |
+| ⛔ AROON - Aroon oscillator || AROON | GetAroon || aroon |
+| ⛔ BOP - Balance of Power || BOP | GetBop || bop |
+| ⭐ CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci || cci |
 | ⛔ CFO - Chande Forcast Oscillator |||||
-| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo ||
+| ⛔ CMO - Chande Momentum Oscillator || CMO | GetCmo || cmo |
 | ⛔ COG - Center of Gravity |||||
 | ⛔ COPPOCK - Coppock Curve |||||
 | ⛔ CTI - Ehler's Correlation Trend Indicator |||||
 | ⛔ DPO - Detrended Price Oscillator ||| GetDpo ||
 | ⛔ DMI - Directional Movement Index || DX | GetAdx ||
 | ⛔ EFI - Elder Ray's Force Index ||| GetElderRay ||
+| ⛔ FOSC - Forecast oscillator ||||| fosc |
 | ⛔ GAT - Alligator oscillator ||| GetGator ||
 | ⛔ HURST - Hurst Exponent ||| GetHurst ||
 | ⛔ KRI - Kairi Relative Index |||||
@@ -176,10 +185,12 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
 | ⛔ WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
 | ⛔ WGAT - Williams Alligator |||||
 ||||||
-| **VOLUME INDICATORS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
+| **VOLUME INDICATORS** |
+||||||
 | ⛔ AOBV - Archer On-Balance Volume |||||
 | ⛔ CMF - Chaikin Money Flow |||||
-| ⛔ EOM - Ease of Movement |||||
+| ⛔ EOM - Ease of Movement ||||| emv |
+| ⛔ KVO - Klinger Volume Oscilaltor ||||| kvo |
 | ⭐ OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
 | ⛔ PRS - Price Relative Strength ||||
 | ⛔ PVOL - Price-Volume |||||