/*! * Chart.js * http://chartjs.org/ * * Copyright 2017 Nick Downie * Released under the MIT license * https://github.com/chartjs/Chart.js/blob/master/LICENSE.md */ !function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Chart=t()}}(function(){return function t(e,n,i){function a(r,l){if(!n[r]){if(!e[r]){var s="function"==typeof require&&require;if(!l&&s)return s(r,!0);if(o)return o(r,!0);var u=new Error("Cannot find module '"+r+"'");throw u.code="MODULE_NOT_FOUND",u}var d=n[r]={exports:{}};e[r][0].call(d.exports,function(t){var n=e[r][1][t];return a(n||t)},d,d.exports,t,e,n,i)}return n[r].exports}for(var o="function"==typeof require&&require,r=0;rn?(e+.05)/(n+.05):(n+.05)/(e+.05)},level:function(t){var e=this.contrast(t);return e>=7.1?"AAA":e>=4.5?"AA":""},dark:function(){var 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e=Math.atan2(r,o),(n=360*e/2/Math.PI)<0&&(n+=360),i=Math.sqrt(o*o+r*r),[a,i,n]}function k(t){return v(x(t))}function w(t){var e,n,i,a=t[0],o=t[1];return i=t[2]/360*2*Math.PI,e=o*Math.cos(i),n=o*Math.sin(i),[a,e,n]}function M(t){return S[t]}e.exports={rgb2hsl:i,rgb2hsv:a,rgb2hwb:o,rgb2cmyk:l,rgb2keyword:s,rgb2xyz:u,rgb2lab:d,rgb2lch:function(t){return y(d(t))},hsl2rgb:c,hsl2hsv:function(t){var e,n,i=t[0],a=t[1]/100,o=t[2]/100;return 0===o?[0,0,0]:(o*=2,a*=o<=1?o:2-o,n=(o+a)/2,e=2*a/(o+a),[i,100*e,100*n])},hsl2hwb:function(t){return o(c(t))},hsl2cmyk:function(t){return l(c(t))},hsl2keyword:function(t){return s(c(t))},hsv2rgb:h,hsv2hsl:function(t){var e,n,i=t[0],a=t[1]/100,o=t[2]/100;return n=(2-a)*o,e=a*o,e/=n<=1?n:2-n,e=e||0,n/=2,[i,100*e,100*n]},hsv2hwb:function(t){return o(h(t))},hsv2cmyk:function(t){return l(h(t))},hsv2keyword:function(t){return s(h(t))},hwb2rgb:f,hwb2hsl:function(t){return i(f(t))},hwb2hsv:function(t){return a(f(t))},hwb2cmyk:function(t){return 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o.each(n.data,function(n,a){t=e.data[a],isNaN(t)||n.hidden||(i+=Math.abs(t))}),i},calculateCircumference:function(t){var e=this.getMeta().total;return e>0&&!isNaN(t)?2*Math.PI*(t/e):0},getMaxBorderWidth:function(t){for(var e,n,i=0,a=this.index,o=t.length,r=0;r(i=e>i?e:i)?n:i;return i}})}},{25:25,40:40,45:45}],18:[function(t,e,n){"use strict";var i=t(25),a=t(40),o=t(45);i._set("line",{showLines:!0,spanGaps:!1,hover:{mode:"label"},scales:{xAxes:[{type:"category",id:"x-axis-0"}],yAxes:[{type:"linear",id:"y-axis-0"}]}}),e.exports=function(t){function e(t,e){return o.valueOrDefault(t.showLine,e.showLines)}t.controllers.line=t.DatasetController.extend({datasetElementType:a.Line,dataElementType:a.Point,update:function(t){var n,i,a,r=this,l=r.getMeta(),s=l.dataset,u=l.data||[],d=r.chart.options,c=d.elements.line,h=r.getScaleForId(l.yAxisID),f=r.getDataset(),g=e(f,d);for(g&&(a=s.custom||{},void 0!==f.tension&&void 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0!==f.fill?f.fill:c.fill,steppedLine:a.steppedLine?a.steppedLine:o.valueOrDefault(f.steppedLine,c.stepped),cubicInterpolationMode:a.cubicInterpolationMode?a.cubicInterpolationMode:o.valueOrDefault(f.cubicInterpolationMode,c.cubicInterpolationMode)},s.pivot()),n=0,i=u.length;n');var n=t.data,i=n.datasets,a=n.labels;if(i.length)for(var o=0;o'),a[o]&&e.push(a[o]),e.push("");return e.push(""),e.join("")},legend:{labels:{generateLabels:function(t){var e=t.data;return e.labels.length&&e.datasets.length?e.labels.map(function(n,i){var a=t.getDatasetMeta(0),r=e.datasets[0],l=a.data[i].custom||{},s=o.valueAtIndexOrDefault,u=t.options.elements.arc;return{text:n,fillStyle:l.backgroundColor?l.backgroundColor:s(r.backgroundColor,i,u.backgroundColor),strokeStyle:l.borderColor?l.borderColor:s(r.borderColor,i,u.borderColor),lineWidth:l.borderWidth?l.borderWidth:s(r.borderWidth,i,u.borderWidth),hidden:isNaN(r.data[i])||a.data[i].hidden,index:i}}):[]}},onClick:function(t,e){var n,i,a,o=e.index,r=this.chart;for(n=0,i=(r.data.datasets||[]).length;n0&&!isNaN(t)?2*Math.PI/e:0}})}},{25:25,40:40,45:45}],20:[function(t,e,n){"use strict";var i=t(25),a=t(40),o=t(45);i._set("radar",{scale:{type:"radialLinear"},elements:{line:{tension:0}}}),e.exports=function(t){t.controllers.radar=t.DatasetController.extend({datasetElementType:a.Line,dataElementType:a.Point,linkScales:o.noop,update:function(t){var e=this,n=e.getMeta(),i=n.dataset,a=n.data,r=i.custom||{},l=e.getDataset(),s=e.chart.options.elements.line,u=e.chart.scale;void 0!==l.tension&&void 0===l.lineTension&&(l.lineTension=l.tension),o.extend(n.dataset,{_datasetIndex:e.index,_scale:u,_children:a,_loop:!0,_model:{tension:r.tension?r.tension:o.valueOrDefault(l.lineTension,s.tension),backgroundColor:r.backgroundColor?r.backgroundColor:l.backgroundColor||s.backgroundColor,borderWidth:r.borderWidth?r.borderWidth:l.borderWidth||s.borderWidth,borderColor:r.borderColor?r.borderColor:l.borderColor||s.borderColor,fill:r.fill?r.fill:void 0!==l.fill?l.fill:s.fill,borderCapStyle:r.borderCapStyle?r.borderCapStyle:l.borderCapStyle||s.borderCapStyle,borderDash:r.borderDash?r.borderDash:l.borderDash||s.borderDash,borderDashOffset:r.borderDashOffset?r.borderDashOffset:l.borderDashOffset||s.borderDashOffset,borderJoinStyle:r.borderJoinStyle?r.borderJoinStyle:l.borderJoinStyle||s.borderJoinStyle}}),n.dataset.pivot(),o.each(a,function(n,i){e.updateElement(n,i,t)},e),e.updateBezierControlPoints()},updateElement:function(t,e,n){var i=this,a=t.custom||{},r=i.getDataset(),l=i.chart.scale,s=i.chart.options.elements.point,u=l.getPointPositionForValue(e,r.data[e]);void 0!==r.radius&&void 0===r.pointRadius&&(r.pointRadius=r.radius),void 0!==r.hitRadius&&void 0===r.pointHitRadius&&(r.pointHitRadius=r.hitRadius),o.extend(t,{_datasetIndex:i.index,_index:e,_scale:l,_model:{x:n?l.xCenter:u.x,y:n?l.yCenter:u.y,tension:a.tension?a.tension:o.valueOrDefault(r.lineTension,i.chart.options.elements.line.tension),radius:a.radius?a.radius:o.valueAtIndexOrDefault(r.pointRadius,e,s.radius),backgroundColor:a.backgroundColor?a.backgroundColor:o.valueAtIndexOrDefault(r.pointBackgroundColor,e,s.backgroundColor),borderColor:a.borderColor?a.borderColor:o.valueAtIndexOrDefault(r.pointBorderColor,e,s.borderColor),borderWidth:a.borderWidth?a.borderWidth:o.valueAtIndexOrDefault(r.pointBorderWidth,e,s.borderWidth),pointStyle:a.pointStyle?a.pointStyle:o.valueAtIndexOrDefault(r.pointStyle,e,s.pointStyle),hitRadius:a.hitRadius?a.hitRadius:o.valueAtIndexOrDefault(r.pointHitRadius,e,s.hitRadius)}}),t._model.skip=a.skip?a.skip:isNaN(t._model.x)||isNaN(t._model.y)},updateBezierControlPoints:function(){var t=this.chart.chartArea,e=this.getMeta();o.each(e.data,function(n,i){var a=n._model,r=o.splineCurve(o.previousItem(e.data,i,!0)._model,a,o.nextItem(e.data,i,!0)._model,a.tension);a.controlPointPreviousX=Math.max(Math.min(r.previous.x,t.right),t.left),a.controlPointPreviousY=Math.max(Math.min(r.previous.y,t.bottom),t.top),a.controlPointNextX=Math.max(Math.min(r.next.x,t.right),t.left),a.controlPointNextY=Math.max(Math.min(r.next.y,t.bottom),t.top),n.pivot()})},setHoverStyle:function(t){var e=this.chart.data.datasets[t._datasetIndex],n=t.custom||{},i=t._index,a=t._model;a.radius=n.hoverRadius?n.hoverRadius:o.valueAtIndexOrDefault(e.pointHoverRadius,i,this.chart.options.elements.point.hoverRadius),a.backgroundColor=n.hoverBackgroundColor?n.hoverBackgroundColor:o.valueAtIndexOrDefault(e.pointHoverBackgroundColor,i,o.getHoverColor(a.backgroundColor)),a.borderColor=n.hoverBorderColor?n.hoverBorderColor:o.valueAtIndexOrDefault(e.pointHoverBorderColor,i,o.getHoverColor(a.borderColor)),a.borderWidth=n.hoverBorderWidth?n.hoverBorderWidth:o.valueAtIndexOrDefault(e.pointHoverBorderWidth,i,a.borderWidth)},removeHoverStyle:function(t){var e=this.chart.data.datasets[t._datasetIndex],n=t.custom||{},i=t._index,a=t._model,r=this.chart.options.elements.point;a.radius=n.radius?n.radius:o.valueAtIndexOrDefault(e.pointRadius,i,r.radius),a.backgroundColor=n.backgroundColor?n.backgroundColor:o.valueAtIndexOrDefault(e.pointBackgroundColor,i,r.backgroundColor),a.borderColor=n.borderColor?n.borderColor:o.valueAtIndexOrDefault(e.pointBorderColor,i,r.borderColor),a.borderWidth=n.borderWidth?n.borderWidth:o.valueAtIndexOrDefault(e.pointBorderWidth,i,r.borderWidth)}})}},{25:25,40:40,45:45}],21:[function(t,e,n){"use strict";t(25)._set("scatter",{hover:{mode:"single"},scales:{xAxes:[{id:"x-axis-1",type:"linear",position:"bottom"}],yAxes:[{id:"y-axis-1",type:"linear",position:"left"}]},showLines:!1,tooltips:{callbacks:{title:function(){return""},label:function(t){return"("+t.xLabel+", "+t.yLabel+")"}}}}),e.exports=function(t){t.controllers.scatter=t.controllers.line}},{25:25}],22:[function(t,e,n){"use strict";var i=t(25),a=t(26),o=t(45);i._set("global",{animation:{duration:1e3,easing:"easeOutQuart",onProgress:o.noop,onComplete:o.noop}}),e.exports=function(t){t.Animation=a.extend({chart:null,currentStep:0,numSteps:60,easing:"",render:null,onAnimationProgress:null,onAnimationComplete:null}),t.animationService={frameDuration:17,animations:[],dropFrames:0,request:null,addAnimation:function(t,e,n,i){var a,o,r=this.animations;for(e.chart=t,i||(t.animating=!0),a=0,o=r.length;a1&&(n=Math.floor(t.dropFrames),t.dropFrames=t.dropFrames%1),t.advance(1+n);var i=Date.now();t.dropFrames+=(i-e)/t.frameDuration,t.animations.length>0&&t.requestAnimationFrame()},advance:function(t){for(var e,n,i=this.animations,a=0;a=e.numSteps?(o.callback(e.onAnimationComplete,[e],n),n.animating=!1,i.splice(a,1)):++a}},Object.defineProperty(t.Animation.prototype,"animationObject",{get:function(){return this}}),Object.defineProperty(t.Animation.prototype,"chartInstance",{get:function(){return this.chart},set:function(t){this.chart=t}})}},{25:25,26:26,45:45}],23:[function(t,e,n){"use strict";var i=t(25),a=t(45),o=t(28),r=t(48);e.exports=function(t){function e(t){var e=(t=t||{}).data=t.data||{};return e.datasets=e.datasets||[],e.labels=e.labels||[],t.options=a.configMerge(i.global,i[t.type],t.options||{}),t}function n(t){var e=t.options;e.scale?t.scale.options=e.scale:e.scales&&e.scales.xAxes.concat(e.scales.yAxes).forEach(function(e){t.scales[e.id].options=e}),t.tooltip._options=e.tooltips}function l(t){return"top"===t||"bottom"===t}var s=t.plugins;t.types={},t.instances={},t.controllers={},a.extend(t.prototype,{construct:function(n,i){var o=this;i=e(i);var l=r.acquireContext(n,i),s=l&&l.canvas,u=s&&s.height,d=s&&s.width;o.id=a.uid(),o.ctx=l,o.canvas=s,o.config=i,o.width=d,o.height=u,o.aspectRatio=u?d/u:null,o.options=i.options,o._bufferedRender=!1,o.chart=o,o.controller=o,t.instances[o.id]=o,Object.defineProperty(o,"data",{get:function(){return o.config.data},set:function(t){o.config.data=t}}),l&&s?(o.initialize(),o.update()):console.error("Failed to create chart: can't acquire context from the given item")},initialize:function(){var t=this;return s.notify(t,"beforeInit"),a.retinaScale(t,t.options.devicePixelRatio),t.bindEvents(),t.options.responsive&&t.resize(!0),t.ensureScalesHaveIDs(),t.buildScales(),t.initToolTip(),s.notify(t,"afterInit"),t},clear:function(){return a.canvas.clear(this),this},stop:function(){return t.animationService.cancelAnimation(this),this},resize:function(t){var e=this,n=e.options,i=e.canvas,o=n.maintainAspectRatio&&e.aspectRatio||null,r=Math.max(0,Math.floor(a.getMaximumWidth(i))),l=Math.max(0,Math.floor(o?r/o:a.getMaximumHeight(i)));if((e.width!==r||e.height!==l)&&(i.width=e.width=r,i.height=e.height=l,i.style.width=r+"px",i.style.height=l+"px",a.retinaScale(e,n.devicePixelRatio),!t)){var u={width:r,height:l};s.notify(e,"resize",[u]),e.options.onResize&&e.options.onResize(e,u),e.stop(),e.update(e.options.responsiveAnimationDuration)}},ensureScalesHaveIDs:function(){var t=this.options,e=t.scales||{},n=t.scale;a.each(e.xAxes,function(t,e){t.id=t.id||"x-axis-"+e}),a.each(e.yAxes,function(t,e){t.id=t.id||"y-axis-"+e}),n&&(n.id=n.id||"scale")},buildScales:function(){var e=this,n=e.options,i=e.scales={},o=[];n.scales&&(o=o.concat((n.scales.xAxes||[]).map(function(t){return{options:t,dtype:"category",dposition:"bottom"}}),(n.scales.yAxes||[]).map(function(t){return{options:t,dtype:"linear",dposition:"left"}}))),n.scale&&o.push({options:n.scale,dtype:"radialLinear",isDefault:!0,dposition:"chartArea"}),a.each(o,function(n){var o=n.options,r=a.valueOrDefault(o.type,n.dtype),s=t.scaleService.getScaleConstructor(r);if(s){l(o.position)!==l(n.dposition)&&(o.position=n.dposition);var u=new s({id:o.id,options:o,ctx:e.ctx,chart:e});i[u.id]=u,u.mergeTicksOptions(),n.isDefault&&(e.scale=u)}}),t.scaleService.addScalesToLayout(this)},buildOrUpdateControllers:function(){var e=this,n=[],i=[];return a.each(e.data.datasets,function(a,o){var r=e.getDatasetMeta(o),l=a.type||e.config.type;if(r.type&&r.type!==l&&(e.destroyDatasetMeta(o),r=e.getDatasetMeta(o)),r.type=l,n.push(r.type),r.controller)r.controller.updateIndex(o);else{var s=t.controllers[r.type];if(void 0===s)throw new Error('"'+r.type+'" is not a chart type.');r.controller=new s(e,o),i.push(r.controller)}},e),i},resetElements:function(){var t=this;a.each(t.data.datasets,function(e,n){t.getDatasetMeta(n).controller.reset()},t)},reset:function(){this.resetElements(),this.tooltip.initialize()},update:function(t){var e=this;if(t&&"object"==typeof t||(t={duration:t,lazy:arguments[1]}),n(e),!1!==s.notify(e,"beforeUpdate")){e.tooltip._data=e.data;var i=e.buildOrUpdateControllers();a.each(e.data.datasets,function(t,n){e.getDatasetMeta(n).controller.buildOrUpdateElements()},e),e.updateLayout(),a.each(i,function(t){t.reset()}),e.updateDatasets(),e.tooltip.initialize(),e.lastActive=[],s.notify(e,"afterUpdate"),e._bufferedRender?e._bufferedRequest={duration:t.duration,easing:t.easing,lazy:t.lazy}:e.render(t)}},updateLayout:function(){var e=this;!1!==s.notify(e,"beforeLayout")&&(t.layoutService.update(this,this.width,this.height),s.notify(e,"afterScaleUpdate"),s.notify(e,"afterLayout"))},updateDatasets:function(){var t=this;if(!1!==s.notify(t,"beforeDatasetsUpdate")){for(var e=0,n=t.data.datasets.length;e=0;--n)e.isDatasetVisible(n)&&e.drawDataset(n,t);s.notify(e,"afterDatasetsDraw",[t])}},drawDataset:function(t,e){var n=this,i=n.getDatasetMeta(t),a={meta:i,index:t,easingValue:e};!1!==s.notify(n,"beforeDatasetDraw",[a])&&(i.controller.draw(e),s.notify(n,"afterDatasetDraw",[a]))},_drawTooltip:function(t){var e=this,n=e.tooltip,i={tooltip:n,easingValue:t};!1!==s.notify(e,"beforeTooltipDraw",[i])&&(n.draw(),s.notify(e,"afterTooltipDraw",[i]))},getElementAtEvent:function(t){return o.modes.single(this,t)},getElementsAtEvent:function(t){return o.modes.label(this,t,{intersect:!0})},getElementsAtXAxis:function(t){return o.modes["x-axis"](this,t,{intersect:!0})},getElementsAtEventForMode:function(t,e,n){var i=o.modes[e];return"function"==typeof i?i(this,t,n):[]},getDatasetAtEvent:function(t){return o.modes.dataset(this,t,{intersect:!0})},getDatasetMeta:function(t){var e=this,n=e.data.datasets[t];n._meta||(n._meta={});var i=n._meta[e.id];return i||(i=n._meta[e.id]={type:null,data:[],dataset:null,controller:null,hidden:null,xAxisID:null,yAxisID:null}),i},getVisibleDatasetCount:function(){for(var t=0,e=0,n=this.data.datasets.length;e0||(a.forEach(function(e){delete t[e]}),delete t._chartjs)}}var a=["push","pop","shift","splice","unshift"];t.DatasetController=function(t,e){this.initialize(t,e)},i.extend(t.DatasetController.prototype,{datasetElementType:null,dataElementType:null,initialize:function(t,e){var n=this;n.chart=t,n.index=e,n.linkScales(),n.addElements()},updateIndex:function(t){this.index=t},linkScales:function(){var t=this,e=t.getMeta(),n=t.getDataset();null===e.xAxisID&&(e.xAxisID=n.xAxisID||t.chart.options.scales.xAxes[0].id),null===e.yAxisID&&(e.yAxisID=n.yAxisID||t.chart.options.scales.yAxes[0].id)},getDataset:function(){return this.chart.data.datasets[this.index]},getMeta:function(){return this.chart.getDatasetMeta(this.index)},getScaleForId:function(t){return this.chart.scales[t]},reset:function(){this.update(!0)},destroy:function(){this._data&&n(this._data,this)},createMetaDataset:function(){var t=this,e=t.datasetElementType;return e&&new e({_chart:t.chart,_datasetIndex:t.index})},createMetaData:function(t){var e=this,n=e.dataElementType;return n&&new n({_chart:e.chart,_datasetIndex:e.index,_index:t})},addElements:function(){var t,e,n=this,i=n.getMeta(),a=n.getDataset().data||[],o=i.data;for(t=0,e=a.length;ti&&t.insertElements(i,a-i)},insertElements:function(t,e){for(var n=0;n=n[e].length&&n[e].push({}),!n[e][r].type||s.type&&s.type!==n[e][r].type?o.merge(n[e][r],[t.scaleService.getScaleDefaults(l),s]):o.merge(n[e][r],s)}else o._merger(e,n,i,a)}})},o.where=function(t,e){if(o.isArray(t)&&Array.prototype.filter)return t.filter(e);var n=[];return o.each(t,function(t){e(t)&&n.push(t)}),n},o.findIndex=Array.prototype.findIndex?function(t,e,n){return t.findIndex(e,n)}:function(t,e,n){n=void 0===n?t:n;for(var i=0,a=t.length;i=0;i--){var a=t[i];if(e(a))return a}},o.isNumber=function(t){return!isNaN(parseFloat(t))&&isFinite(t)},o.almostEquals=function(t,e,n){return Math.abs(t-e)t},o.max=function(t){return t.reduce(function(t,e){return isNaN(e)?t:Math.max(t,e)},Number.NEGATIVE_INFINITY)},o.min=function(t){return t.reduce(function(t,e){return isNaN(e)?t:Math.min(t,e)},Number.POSITIVE_INFINITY)},o.sign=Math.sign?function(t){return Math.sign(t)}:function(t){return 0==(t=+t)||isNaN(t)?t:t>0?1:-1},o.log10=Math.log10?function(t){return Math.log10(t)}:function(t){return Math.log(t)/Math.LN10},o.toRadians=function(t){return t*(Math.PI/180)},o.toDegrees=function(t){return t*(180/Math.PI)},o.getAngleFromPoint=function(t,e){var n=e.x-t.x,i=e.y-t.y,a=Math.sqrt(n*n+i*i),o=Math.atan2(i,n);return o<-.5*Math.PI&&(o+=2*Math.PI),{angle:o,distance:a}},o.distanceBetweenPoints=function(t,e){return Math.sqrt(Math.pow(e.x-t.x,2)+Math.pow(e.y-t.y,2))},o.aliasPixel=function(t){return t%2==0?0:.5},o.splineCurve=function(t,e,n,i){var a=t.skip?e:t,o=e,r=n.skip?e:n,l=Math.sqrt(Math.pow(o.x-a.x,2)+Math.pow(o.y-a.y,2)),s=Math.sqrt(Math.pow(r.x-o.x,2)+Math.pow(r.y-o.y,2)),u=l/(l+s),d=s/(l+s),c=i*(u=isNaN(u)?0:u),h=i*(d=isNaN(d)?0:d);return{previous:{x:o.x-c*(r.x-a.x),y:o.y-c*(r.y-a.y)},next:{x:o.x+h*(r.x-a.x),y:o.y+h*(r.y-a.y)}}},o.EPSILON=Number.EPSILON||1e-14,o.splineCurveMonotone=function(t){var e,n,i,a,r=(t||[]).map(function(t){return{model:t._model,deltaK:0,mK:0}}),l=r.length;for(e=0;e0?r[e-1]:null,(a=e0?r[e-1]:null,a=e=t.length-1?t[0]:t[e+1]:e>=t.length-1?t[t.length-1]:t[e+1]},o.previousItem=function(t,e,n){return n?e<=0?t[t.length-1]:t[e-1]:e<=0?t[0]:t[e-1]},o.niceNum=function(t,e){var n=Math.floor(o.log10(t)),i=t/Math.pow(10,n);return(e?i<1.5?1:i<3?2:i<7?5:10:i<=1?1:i<=2?2:i<=5?5:10)*Math.pow(10,n)},o.requestAnimFrame="undefined"==typeof 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i(this)?(t=n.x,e=(n.y+n.base)/2):(t=(n.x+n.base)/2,e=n.y),{x:t,y:e}},getArea:function(){var t=this._view;return t.width*Math.abs(t.y-t.base)},tooltipPosition:function(){var t=this._view;return{x:t.x,y:t.y}}})},{25:25,26:26}],40:[function(t,e,n){"use strict";e.exports={},e.exports.Arc=t(36),e.exports.Line=t(37),e.exports.Point=t(38),e.exports.Rectangle=t(39)},{36:36,37:37,38:38,39:39}],41:[function(t,e,n){"use strict";var i=t(42),n=e.exports={clear:function(t){t.ctx.clearRect(0,0,t.width,t.height)},roundedRect:function(t,e,n,i,a,o){if(o){var r=Math.min(o,i/2),l=Math.min(o,a/2);t.moveTo(e+r,n),t.lineTo(e+i-r,n),t.quadraticCurveTo(e+i,n,e+i,n+l),t.lineTo(e+i,n+a-l),t.quadraticCurveTo(e+i,n+a,e+i-r,n+a),t.lineTo(e+r,n+a),t.quadraticCurveTo(e,n+a,e,n+a-l),t.lineTo(e,n+l),t.quadraticCurveTo(e,n,e+r,n)}else t.rect(e,n,i,a)},drawPoint:function(t,e,n,i,a){var o,r,l,s,u,d;if(!e||"object"!=typeof e||"[object HTMLImageElement]"!==(o=e.toString())&&"[object 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Considerable_gains_around_pickwin_for_seasoned_traders_and_newcomers_alike

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Considerable gains around pickwin for seasoned traders and newcomers alike

The world of trading presents a myriad of opportunities, particularly for those seeking to enhance their strategies with specialized tools and insights. Among these, the concept of has gained traction, offering a unique approach to identifying potentially profitable trading scenarios. It's a methodology built on a combination of technical analysis, market sentiment, and a data-driven approach to risk management. Understanding the nuances of this technique can be beneficial for both experienced traders looking to refine their skills and newcomers eager to build a solid foundation in the financial markets.

However, it's crucial to approach any trading strategy, including those centered around , with a realistic mindset. The market is inherently unpredictable, and no system guarantees consistent profits. Successful trading requires discipline, continuous learning, and a willingness to adapt to changing market conditions. This article delves into the core principles behind , exploring its methodologies, pickwin potential benefits, associated risks, and practical applications. It aims to provide a comprehensive overview, equipping readers with the knowledge necessary to assess whether this approach aligns with their individual trading goals and risk tolerance.

Understanding the Core Principles of Pickwin

At its heart, the methodology is centered on identifying specific patterns and conditions that historically suggest a high probability of a favorable price movement. This isn’t simply about finding “sure things” – such a concept doesn’t exist in trading – but rather about increasing the odds in your favor. The system attempts to synthesize a variety of analytical techniques, including trend following, momentum investing, and support and resistance level identification. It aims to filter out noise and focus on signals that have proven reliable over time through backtesting and historical data analysis. This focus on data and objective criteria is a key differentiator for many proponents of the approach, distinguishing it from more subjective or intuition-based trading styles.

A core component of the system involves a weighting system, assigning different values to various indicators. These indicators can range from moving averages and relative strength index (RSI) to volume and volatility measurements. The weighting assigned to each indicator is determined by its historical performance and correlation with successful trades. This dynamic weighting ensures the system adapts to changing market conditions and remains responsive to evolving trends. Furthermore, risk management is integral to the process. The system often incorporates specific rules for position sizing, stop-loss orders, and profit targets, designed to protect capital and minimize potential losses.

The Role of Backtesting and Optimization

Before implementing any trading strategy, thorough backtesting is essential. Backtesting involves applying the strategy to historical data to assess its performance under various market conditions. This process helps identify potential weaknesses, optimize parameters, and gain confidence in the system’s viability. With , backtesting is particularly crucial due to the complex interplay of multiple indicators and the dynamic weighting system. Careful optimization is necessary to ensure the system is not overfitted to historical data, which can lead to poor performance in live trading. The goal is to find a balance between capturing historical trends and maintaining adaptability to future market fluctuations. The results of backtesting should be analyzed critically, considering factors such as transaction costs, slippage, and the limitations of historical data.

It’s also vital to understand that past performance is not indicative of future results. While backtesting provides valuable insights, it cannot perfectly replicate the complexities of live trading. Market conditions constantly evolve, and unforeseen events can significantly impact trading outcomes. Therefore, continuous monitoring and adaptation are crucial for maintaining the effectiveness of the system.

Indicator
Weighting (Example)
Description
Moving Average Convergence Divergence (MACD) 25% Identifies trend direction and potential momentum shifts.
Relative Strength Index (RSI) 20% Measures the magnitude of recent price changes to evaluate overbought or oversold conditions.
Volume 15% Confirms the strength of price movements.
Volatility (ATR) 10% Indicates the degree of price fluctuation.
Support & Resistance Levels 30% Identifies potential price reversal points.

The table above presents a simplified example of indicator weighting within the framework. Actual weighting varies based on market analysis and optimization processes.

Identifying Potential Trading Signals with Pickwin

The process of generating trading signals using typically involves a multi-step approach. First, the system scans the market for assets that meet specific criteria based on the weighted indicators. This might include identifying stocks with a bullish MACD crossover, an RSI approaching oversold levels, and increasing volume. These preliminary screens help narrow down the universe of potential trading candidates. Next, the system assesses the proximity of price to key support and resistance levels. The ideal scenario often involves a price nearing a significant support level, suggesting a potential buying opportunity. Furthermore, the system considers overall market context, taking into account factors such as economic news, geopolitical events, and sector-specific trends. A confluence of positive signals across multiple indicators increases the likelihood of a successful trade according to the methodology.

It’s important to note that a trading signal generated by is not a guaranteed buy or sell recommendation. Instead, it serves as a starting point for further analysis. Traders are encouraged to conduct their own due diligence, considering factors such as company fundamentals, industry outlook, and their own risk tolerance. The system is designed to provide objective signals, but ultimately, the decision to execute a trade rests with the individual trader.

Combining Pickwin with Fundamental Analysis

While is primarily a technical analysis-based system, combining it with fundamental analysis can significantly enhance its effectiveness. Fundamental analysis involves evaluating the intrinsic value of an asset by examining factors such as revenue, earnings, debt, and management quality. By integrating fundamental analysis into the process, traders can filter out potentially weak assets and focus on those with strong underlying fundamentals. For example, a stock that generates a positive signal might be a more attractive investment if it also boasts healthy financial ratios and a positive growth outlook. This holistic approach reduces the risk of trading based solely on technical signals and increases the probability of identifying long-term investment opportunities.

However, it’s essential to be mindful of potential conflicts between technical and fundamental signals. Sometimes, a stock might appear technically overbought but still possess strong fundamental characteristics. In such cases, traders need to weigh the relative importance of each factor and make a judgment based on their overall assessment.

  • Identify assets meeting initial criteria (e.g., MACD, RSI, Volume).
  • Assess proximity to support and resistance levels.
  • Evaluate overall market context (economic news, geopolitical events).
  • Conduct fundamental analysis (revenue, earnings, debt).
  • Combine technical and fundamental insights to make informed trading decisions.

The list above outlines the main steps in combining with fundamental analysis for enhanced trading signals.

Risk Management Strategies within the Pickwin Framework

Effective risk management is paramount in any trading strategy, and the methodology is no exception. Given the inherent unpredictability of the market, it’s crucial to protect capital and minimize potential losses. One key risk management technique employed within the framework is position sizing. This involves determining the appropriate amount of capital to allocate to each trade based on factors such as risk tolerance, account size, and the potential reward-to-risk ratio. A common rule of thumb is to risk no more than 1-2% of your trading capital on any single trade. This helps prevent a single losing trade from significantly impacting your overall portfolio performance.

Another crucial aspect of risk management is the use of stop-loss orders. A stop-loss order is an instruction to automatically sell an asset if it reaches a predetermined price level. This helps limit potential losses if the trade moves against you. The placement of stop-loss orders is a critical decision and should be based on technical analysis, volatility, and the overall market context. Furthermore, traders should consider using take-profit orders to lock in profits when the trade reaches a desired price target. This helps ensure that profits are realized and prevents them from evaporating if the market reverses.

Diversification and Correlation Analysis

Diversification is a fundamental principle of risk management that involves spreading investments across a variety of assets. This reduces the risk of losing all your capital if a single asset performs poorly. Within the framework, diversification can be achieved by trading a range of different assets across various sectors and markets. However, it’s important to consider the correlation between different assets. Correlation measures the degree to which two assets move in relation to each other. Trading assets that are highly correlated does not provide significant diversification benefits. Instead, traders should focus on selecting assets that have low or negative correlations, ensuring that their portfolio is well-balanced and resilient to market fluctuations.

Regularly reviewing and adjusting your portfolio is also vital. Market conditions change over time, and the correlation between assets can shift. It’s essential to monitor your portfolio’s performance and make adjustments as needed to maintain your desired level of diversification.

  1. Determine appropriate position size (risk no more than 1-2% of capital per trade).
  2. Utilize stop-loss orders to limit potential losses.
  3. Set take-profit orders to lock in profits.
  4. Diversify across a range of assets.
  5. Analyze correlation between different assets.
  6. Regularly review and adjust your portfolio.

This list highlights the key risk management practices within the system.

Advanced Applications of Pickwin: Algorithmic Trading

The methodology lends itself well to algorithmic trading, where trading decisions are automated based on predefined rules and criteria. By coding the system into an algorithm, traders can eliminate emotional biases, execute trades with precision, and backtest strategies more efficiently. Algorithmic trading allows for the rapid analysis of vast amounts of market data and the identification of trading opportunities that might be missed by human traders. However, it's crucial to thoroughly test and validate the algorithm before deploying it in a live trading environment. Bugs or errors in the code can lead to unintended consequences and significant financial losses.

Furthermore, algorithmic trading requires ongoing monitoring and maintenance. Market conditions can change, and the algorithm may need to be adjusted to maintain its effectiveness. It’s also essential to be aware of the potential for unforeseen events that could disrupt the algorithm’s performance. System outages, data errors, or unexpected market volatility can all trigger unexpected behavior. A robust risk management framework is particularly crucial in algorithmic trading to mitigate these risks.

Beyond the Basics: Integrating Pickwin with AI and Machine Learning

The future of may involve integrating it with more advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML). AI and ML algorithms can analyze vast datasets, identify complex patterns, and adapt to changing market conditions in ways that traditional statistical methods cannot. For example, ML algorithms could be used to optimize the weighting of indicators within the system, dynamically adjusting to maximize profitability. AI could also be employed to predict market movements and identify emerging trends, providing traders with a valuable edge. The ability to process and interpret complex data in real-time offers a significant advantage in today’s fast-paced financial markets. However, it's important to note that AI and ML are not panaceas. These technologies require significant data, expertise, and ongoing maintenance. The "black box" nature of some ML algorithms can also make it difficult to understand why certain trading decisions are being made.

The ethical implications of using AI in trading also need to be considered. Ensuring fairness, transparency, and accountability are crucial for maintaining trust and preventing market manipulation. As AI and ML become more prevalent in trading, regulatory frameworks will need to evolve to address these challenges.

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