{"id":45457,"date":"2026-06-22T22:27:27","date_gmt":"2026-06-23T03:27:27","guid":{"rendered":"https:\/\/library-staging.tradingtechnologies.com\/doc\/disparity-index-2\/"},"modified":"2026-06-22T22:27:27","modified_gmt":"2026-06-23T03:27:27","slug":"disparity-index-2","status":"publish","type":"doc","link":"https:\/\/library-staging.tradingtechnologies.com\/ja\/disparity-index-2\/","title":{"rendered":"\u30c7\u30a3\u30b9\u30d1\u30ea\u30c6\u30a3 \u30a4\u30f3\u30c7\u30c3\u30af\u30b9"},"content":{"rendered":"<p class=\"BodyOther\">\u30c7\u30a3\u30b9\u30d1\u30ea\u30c6\u30a3 \u30a4\u30f3\u30c7\u30c3\u30af\u30b9 (Disparity Index) \u6307\u6a19\u306f\u3001\u6307\u5b9a\u3057\u305f\u79fb\u52d5\u5e73\u5747\u306b\u5bfe\u3057\u3066\u6700\u65b0\u306e\u7d42\u5024\u306e\u76f8\u5bfe\u30dd\u30b8\u30b7\u30e7\u30f3\u3092\u6e2c\u5b9a\u3057\u3001\u30d1\u30fc\u30bb\u30f3\u30c8\u6570\u3067\u5024\u3092\u8a18\u9332\u3057\u307e\u3059\u30020 \u4ee5\u4e0a\u306e\u5024\u306f\u3001\u30a2\u30bb\u30c3\u30c8\u304c\u4e0a\u5411\u304d\u306e\u30e2\u30e1\u30f3\u30bf\u30e0\u3067\u5f97\u3066\u3044\u308b\u4e00\u65b9\u30010 \u4ee5\u4e0b\u306e\u5024\u306f\u3001\u58f2\u308a\u306e\u5727\u529b\u304c\u4e0a\u6607\u3057\u3066\u3044\u308b\u5146\u5019\u3068\u3057\u3066\u89e3\u91c8\u3067\u304d\u307e\u3059\u3002<\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/disparity-index-2.png\" alt=\"\"><\/p>\n<h2>Configuration Options<\/h2>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/disparity-index-3.png\" alt=\"\"><\/p>\n<ul>\n<li>  \t<strong>\u6b04<\/strong>: \u5e73\u5747\u8a08\u7b97\u306e\u30d9\u30fc\u30b9\u306b\u4f7f\u7528\u3059\u308b\u4fa1\u683c\u3001\u307e\u305f\u306f\u4fa1\u683c\u306e\u7d44\u307f\u5408\u308f\u305b\u3002\u4ee5\u4e0b\u306e\u5024\u304c\u3042\u308a\u307e\u3059\u3002\n<ul>\n<li>Open (\u59cb\u5024)<\/li>\n<li>High (\u9ad8\u5024)<\/li>\n<li>Low (\u5b89\u5024)<\/li>\n<li>Close (\u7d42\u5024)<\/li>\n<li>Adjusted Close (\u8abf\u6574\u7d42\u5024)<\/li>\n<li>HL\/2 ( left ( frac{High + Low}{2} right ) )<\/li>\n<li>HLC\/3 ( left ( frac{High + Low + Close}{3} right ) )<\/li>\n<li>HLCC\/4 ( left ( frac{High + Low + Close + Close}{4} right ) )<\/li>\n<li>OHLC\/4 ( left ( frac{Open + High + Low + Close}{4} right ) )<\/li>\n<\/ul>\n<\/li>\n<li><strong>Period<\/strong> (\u30d4\u30ea\u30aa\u30c9): \u8a08\u7b97\u3067\u4f7f\u7528\u3055\u308c\u308b\u30d0\u30fc\u6570\u3002<\/li>\n<li>  \t<strong>Moving Average Type<\/strong>: Type of moving average to use in the calculations:\n<ul hidden>\n<li><strong>Simple<\/strong>: Mean (average) of the data.<\/li>\n<li><strong>Exponential<\/strong>: Newer data are weighted more heavily geometrically.<\/li>\n<li><strong>Time Series<\/strong>: Calculates a linear regression trendline using the \u201cleast squares fit\u201d method.<\/li>\n<li><strong>Triangular<\/strong>: Weighted average where the middle data are given the most weight, decreasing linearly to the end points.<\/li>\n<li><strong>Variable<\/strong>: An exponential moving average with a volatility index factored into the smoothing formula.  The Variable Moving average uses the Chande Momentum Oscillator as the volatility index.<\/li>\n<li><strong>VIDYA<\/strong>: An exponential moving average with a volatility index factored into the smoothing formula.  The VIDYA moving average uses the Standard Deviation as the volatility index. (Volatility Index DYnamic Average).<\/li>\n<li><strong>Weighted<\/strong>: Newer data are weighted more heavily arithmetically.<\/li>\n<li><strong>Welles Winder<\/strong>:The standard exponential moving average formula converts the time period to a fraction using the formula EMA% = 2\/(n + 1) where n is the number of days. For example, the EMA% for 14 days is 2\/(14 days +1) = 13.3%. Wilder, however, uses an EMA% of 1\/14 (1\/n) which equals 7.1%. This equates to a 27-day exponential moving average using the standard formula.<\/li>\n<li><strong>Hull<\/strong>: The Hull Moving Average makes a moving average more responsive while maintaining a curve smoothness. The formula for calculating this average is as follows: HMA[i] = MA( (2*MA(input, period\/2) \u2013 MA(input, period)), SQRT(period)) where MA is a moving average and SQRT is square root.<\/li>\n<li><strong>Double Exponential<\/strong>: The Double Exponential moving average attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values.<\/li>\n<li><strong>Triple Exponential<\/strong>: TBD<\/li>\n<\/ul>\n<ul>\n<li>Simple<\/li>\n<li>Exponential<\/li>\n<li>Time Series<\/li>\n<li>Triangular<\/li>\n<li>Variable<\/li>\n<li>VIDYA<\/li>\n<li>Weighted<\/li>\n<li>Welles Winder<\/li>\n<li>Hull<\/li>\n<li>Double Exponential<\/li>\n<li>Triple Exponential<\/li>\n<\/ul>\n<\/li>\n<li><strong>Color Selectors<\/strong> (\u914d\u8272\u30bb\u30ec\u30af\u30bf\u30fc): \u30b0\u30e9\u30d5\u8981\u7d20\u306b\u4f7f\u7528\u3059\u308b\u914d\u8272\u3002<\/li>\n<li><strong>Display Axis Label<\/strong> (\u8ef8\u30e9\u30d9\u30eb\u306e\u8868\u793a): Y \u8ef8\u306b\u6700\u65b0\u5024\u3092\u8868\u793a\u3059\u308b\u304b\u3069\u3046\u304b\u3002<\/li>\n<\/ul>\n<p><a name=\"Formula\"><\/a><\/p>\n<h2 class=\"Blurb\">\u6570\u5f0f<\/h2>\n<p>[ DI = frac{C-Mov(C,X,MA)}{Mov(C,X,MA) times 100} ]<\/p>\n<p><!--\n<a name=\"Example\"><\/a>\n\n\n<h5 class=\"Blurb\">Example<\/h5>\n\n\n\n\n<p class=\"\">\n\t<img decoding=\"async\" src=\"Content\/Technical_Indicator_Definitions142.jpg\">\n\t\n\t<\/img>\n<\/p>\n\n--><\/p>\n","protected":false},"excerpt":{"rendered":"<p class=\"BodyOther\">\u30c7\u30a3\u30b9\u30d1\u30ea\u30c6\u30a3 \u30a4\u30f3\u30c7\u30c3\u30af\u30b9 (Disparity Index) \u6307\u6a19\u306f\u3001\u6307\u5b9a\u3057\u305f\u79fb\u52d5\u5e73\u5747\u306b\u5bfe\u3057\u3066\u6700\u65b0\u306e\u7d42\u5024\u306e\u76f8\u5bfe\u30dd\u30b8\u30b7\u30e7\u30f3\u3092\u6e2c\u5b9a\u3057\u3001\u30d1\u30fc\u30bb\u30f3\u30c8\u6570\u3067\u5024\u3092\u8a18\u9332\u3057\u307e\u3059\u30020 \u4ee5\u4e0a\u306e\u5024\u306f\u3001\u30a2\u30bb\u30c3\u30c8\u304c\u4e0a\u5411\u304d\u306e\u30e2\u30e1\u30f3\u30bf\u30e0\u3067\u5f97\u3066\u3044\u308b\u4e00\u65b9\u30010 \u4ee5\u4e0b\u306e\u5024\u306f\u3001\u58f2\u308a\u306e\u5727\u529b\u304c\u4e0a\u6607\u3057\u3066\u3044\u308b\u5146\u5019\u3068\u3057\u3066\u89e3\u91c8\u3067\u304d\u307e\u3059\u3002<\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/disparity-index-2.png\" alt=\"\"><\/p>\n<h2>Configuration Options<\/h2>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/disparity-index-3.png\" alt=\"\"><\/p>\n<ul>\n<li>  \t<strong>\u6b04<\/strong>: \u5e73\u5747\u8a08\u7b97\u306e\u30d9\u30fc\u30b9\u306b\u4f7f\u7528\u3059\u308b\u4fa1\u683c\u3001\u307e\u305f\u306f\u4fa1\u683c\u306e\u7d44\u307f\u5408\u308f\u305b\u3002\u4ee5\u4e0b\u306e\u5024\u304c\u3042\u308a\u307e\u3059\u3002\n<ul>\n<li>Open (\u59cb\u5024)<\/li>\n<li>High (\u9ad8\u5024)<\/li>\n<li>Low (\u5b89\u5024)<\/li>\n<li>Close (\u7d42\u5024)<\/li>\n<li>Adjusted Close (\u8abf\u6574\u7d42\u5024)<\/li>\n<li>HL\/2 ( left ( frac{High + Low}{2} right ) )<\/li>\n<li>HLC\/3 ( left ( frac{High + Low + Close}{3} right ) )<\/li>\n<li>HLCC\/4 ( left ( frac{High + Low + Close + Close}{4} right ) )<\/li>\n<li>OHLC\/4 ( left ( frac{Open + High + Low + Close}{4} right ) )<\/li>\n<\/ul>\n<\/li>\n<li><strong>Period<\/strong> (\u30d4\u30ea\u30aa\u30c9): \u8a08\u7b97\u3067\u4f7f\u7528\u3055\u308c\u308b\u30d0\u30fc\u6570\u3002<\/li>\n<li>  \t<strong>Moving Average Type<\/strong>: Type of moving average to use in the calculations:\n<ul hidden>\n<li><strong>Simple<\/strong>: Mean (average) of the data.<\/li>\n<li><strong>Exponential<\/strong>: Newer data are weighted more heavily geometrically.<\/li>\n<li><strong>Time Series<\/strong>: Calculates a linear regression trendline using the \u201cleast squares fit\u201d method.<\/li>\n<li><strong>Triangular<\/strong>: Weighted average where the middle data are given the most weight, decreasing linearly to the end points.<\/li>\n<li><strong>Variable<\/strong>: An exponential moving average with a volatility index factored into the smoothing formula.  The Variable Moving average uses the Chande Momentum Oscillator as the volatility index.<\/li>\n<li><strong>VIDYA<\/strong>: An exponential moving average with a volatility index factored into the smoothing formula.  The VIDYA moving average uses the Standard Deviation as the volatility index. (Volatility Index DYnamic Average).<\/li>\n<li><strong>Weighted<\/strong>: Newer data are weighted more heavily arithmetically.<\/li>\n<li><strong>Welles Winder<\/strong>:The standard exponential moving average formula converts the time period to a fraction using the formula EMA% = 2\/(n + 1) where n is the number of days. For example, the EMA% for 14 days is 2\/(14 days +1) = 13.3%. Wilder, however, uses an EMA% of 1\/14 (1\/n) which equals 7.1%. This equates to a 27-day exponential moving average using the standard formula.<\/li>\n<li><strong>Hull<\/strong>: The Hull Moving Average makes a moving average more responsive while maintaining a curve smoothness. The formula for calculating this average is as follows: HMA[i] = MA( (2*MA(input, period\/2) \u2013 MA(input, period)), SQRT(period)) where MA is a moving average and SQRT is square root.<\/li>\n<li><strong>Double Exponential<\/strong>: The Double Exponential moving average attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values.<\/li>\n<li><strong>Triple Exponential<\/strong>: TBD<\/li>\n<\/ul>\n<ul>\n<li>Simple<\/li>\n<li>Exponential<\/li>\n<li>Time Series<\/li>\n<li>Triangular<\/li>\n<li>Variable<\/li>\n<li>VIDYA<\/li>\n<li>Weighted<\/li>\n<li>Welles Winder<\/li>\n<li>Hull<\/li>\n<li>Double Exponential<\/li>\n<li>Triple Exponential<\/li>\n<\/ul>\n<\/li>\n<li><strong>Color Selectors<\/strong> (\u914d\u8272\u30bb\u30ec\u30af\u30bf\u30fc): \u30b0\u30e9\u30d5\u8981\u7d20\u306b\u4f7f\u7528\u3059\u308b\u914d\u8272\u3002<\/li>\n<li><strong>Display Axis Label<\/strong> (\u8ef8\u30e9\u30d9\u30eb\u306e\u8868\u793a): Y \u8ef8\u306b\u6700\u65b0\u5024\u3092\u8868\u793a\u3059\u308b\u304b\u3069\u3046\u304b\u3002<\/li>\n<\/ul>\n<p><a name=\"Formula\"><\/a><\/p>\n<h2 class=\"Blurb\">\u6570\u5f0f<\/h2>\n<p>[ DI = frac{C-Mov(C,X,MA)}{Mov(C,X,MA) times 100} ]<\/p>\n<p><!--\n<a name=\"Example\"><\/a>\n\n\n\n\n<h5 class=\"Blurb\">Example<\/h5>\n\n\n\n\n\n\n\n\n<p class=\"\">\n\t<img decoding=\"async\" src=\"Content\/Technical_Indicator_Definitions142.jpg\">\n\t\n\t<\/img>\n<\/p>\n\n\n\n--><\/p>\n","protected":false},"author":2,"template":"","meta":{"_acf_changed":false,"footnotes":""},"docs-category":[375],"class_list":["post-45457","doc","type-doc","status-publish","hentry","docs-category-technical-indicators"],"acf":[],"_links":{"self":[{"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/doc\/45457","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/doc"}],"about":[{"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/types\/doc"}],"author":[{"embeddable":true,"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/users\/2"}],"version-history":[{"count":0,"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/doc\/45457\/revisions"}],"wp:attachment":[{"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/media?parent=45457"}],"wp:term":[{"taxonomy":"docs-category","embeddable":true,"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/docs-category?post=45457"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}