{"id":45412,"date":"2026-06-22T22:26:42","date_gmt":"2026-06-23T03:26:42","guid":{"rendered":"https:\/\/library-staging.tradingtechnologies.com\/doc\/balance-of-power-2\/"},"modified":"2026-06-22T22:26:43","modified_gmt":"2026-06-23T03:26:43","slug":"balance-of-power-2","status":"publish","type":"doc","link":"https:\/\/library-staging.tradingtechnologies.com\/ja\/balance-of-power-2\/","title":{"rendered":"\u30d0\u30e9\u30f3\u30b9 \u30aa\u30d6 \u30d1\u30ef\u30fc"},"content":{"rendered":"<p class=\"BodyOther\">\u30d0\u30e9\u30f3\u30b9 \u30aa\u30d6 \u30d1\u30ef\u30fc (BOP) \u6307\u6a19\u306f\u3001\u8cb7\u3044\u306e\u529b\u3068\u58f2\u308a\u306e\u5146\u5019\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u3067\u3001\u4fa1\u683c\u30c8\u30ec\u30f3\u30c9\u3092\u6e2c\u5b9a\u3057\u3001\u4fa1\u683c\u304c\u3069\u306e\u304f\u3089\u3044\u306e\u5f37\u3055\u3067\u6975\u7aef\u306a\u9ad8\u5024\u5e2f\u3084\u5b89\u5024\u5e2f\u306b\u79fb\u52d5\u3059\u308b\u304b\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002BOP \u306f -1 \uff5e 1 \u306e\u9593\u3067\u632f\u52d5\u3057\u3001\u6b63\u306e\u5024\u306f\u5f37\u3044\u8cb7\u3044\u306e\u5727\u529b\u3092\u793a\u3057\u3001\u8ca0\u306e\u5024\u306f\u5f37\u3044\u58f2\u308a\u306e\u5727\u529b\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u6307\u6a19\u304c 0 \u306b\u8fd1\u3065\u304f\u3068\u3001\u8cb7\u3044\u624b\u3068\u58f2\u308a\u624b\u306e\u529b\u304c\u5747\u4e00\u306b\u306a\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/balance-of-power-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\/balance-of-power-3.png\" alt=\"\"><\/p>\n<ul>\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 hidden>BOP = SMA of [ (Close &#8211; Open) \/ (High &#8211; Low) ]<\/p>\n<p>[BOP = SMA;of left [ frac{Close &#8211; Open}{High &#8211; Low} right ] ]<\/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\">\u30d0\u30e9\u30f3\u30b9 \u30aa\u30d6 \u30d1\u30ef\u30fc (BOP) \u6307\u6a19\u306f\u3001\u8cb7\u3044\u306e\u529b\u3068\u58f2\u308a\u306e\u5146\u5019\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u3067\u3001\u4fa1\u683c\u30c8\u30ec\u30f3\u30c9\u3092\u6e2c\u5b9a\u3057\u3001\u4fa1\u683c\u304c\u3069\u306e\u304f\u3089\u3044\u306e\u5f37\u3055\u3067\u6975\u7aef\u306a\u9ad8\u5024\u5e2f\u3084\u5b89\u5024\u5e2f\u306b\u79fb\u52d5\u3059\u308b\u304b\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002BOP \u306f -1 \uff5e 1 \u306e\u9593\u3067\u632f\u52d5\u3057\u3001\u6b63\u306e\u5024\u306f\u5f37\u3044\u8cb7\u3044\u306e\u5727\u529b\u3092\u793a\u3057\u3001\u8ca0\u306e\u5024\u306f\u5f37\u3044\u58f2\u308a\u306e\u5727\u529b\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u6307\u6a19\u304c 0 \u306b\u8fd1\u3065\u304f\u3068\u3001\u8cb7\u3044\u624b\u3068\u58f2\u308a\u624b\u306e\u529b\u304c\u5747\u4e00\u306b\u306a\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/library-staging.tradingtechnologies.com\/wp-content\/uploads\/2026\/06\/balance-of-power-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\/balance-of-power-3.png\" alt=\"\"><\/p>\n<ul>\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 hidden>BOP = SMA of [ (Close &#8211; Open) \/ (High &#8211; Low) ]<\/p>\n<p>[BOP = SMA;of left [ frac{Close &#8211; Open}{High &#8211; Low} right ] ]<\/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-45412","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\/45412","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\/45412\/revisions"}],"wp:attachment":[{"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/media?parent=45412"}],"wp:term":[{"taxonomy":"docs-category","embeddable":true,"href":"https:\/\/library-staging.tradingtechnologies.com\/ja\/wp-json\/wp\/v2\/docs-category?post=45412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}