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<a class="pull-left" href="https://bspeice.github.io/"><img class="mr20" src="https://bspeice.github.io/images/logo.svg" alt="logo">Bradlee Speice</a>
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<h1 class="header-title">Autocallable Bonds</h1>
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<p class="header-date"> <a href="https://bspeice.github.io/author/bradlee-speice.html">Bradlee Speice</a>, Fri 27 November 2015, <a href="https://bspeice.github.io/category/blog.html">Blog</a></p>
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<a href="https://bspeice.github.io/tag/finance.html">finance</a>, <a href="https://bspeice.github.io/tag/monte-carlo.html">monte carlo</a>, <a href="https://bspeice.github.io/tag/simulation.html">simulation</a> </p>
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<p>My only non-core class this semester has been in Structure Products. We've been surveying a wide variety of products, and the final project was to pick one to report on.
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Because these are all very similar, we decided to demonstrate all 3 products at once.</p>
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<p>What follows below is a notebook demonstrating the usage of <a href="http://julialang.com">Julia</a> for Monte-Carlo simulation of some exotic products.</p>
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<p>
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<div class="prompt input_prompt">In [1]:</div>
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<div class=" highlight hl-julia"><pre><span class="k">using</span> <span class="n">Gadfly</span>
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</pre></div>
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<h1 id="Athena/Phoenix-Simulation">Athena/Phoenix Simulation<a class="anchor-link" href="#Athena/Phoenix-Simulation">¶</a></h1><h2 id="Underlying-simulation">Underlying simulation<a class="anchor-link" href="#Underlying-simulation">¶</a></h2><p>In order to price the autocallable bonds, we need to simulate the underlying assets. Let's go ahead and set up the simulation first, as this lays the foundation for what we're trying to do. We're going to use <a href="http://finance.yahoo.com/q?s=jnj">JNJ</a> as the basis for our simulation. This implies the following parameters:</p>
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<ul>
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<li>$S_0$ = \$102.2 (as of time of writing)</li>
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<li>$q$ = 2.84%</li>
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<li>$r$ = [.49, .9, 1.21, 1.45, 1.69] (term structure as of time of writing, linear interpolation)</li>
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<li>$\mu$ = $r - q$ (note that this implies a negative drift because of current low rates)</li>
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<li>$\sigma$ = $\sigma_{imp}$ = 15.62% (from VIX implied volatility)</li>
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</ul>
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<p>We additionally define some parameters for simulation:</p>
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<ul>
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<li><code>T</code>: The number of years to simulate</li>
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<li><code>m</code>: The number of paths to simulate</li>
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<li><code>n</code>: The number of steps to simulate in a year</li>
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</ul>
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<div class=" highlight hl-julia"><pre><span class="n">S0</span> <span class="o">=</span> <span class="mf">102.2</span>
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<span class="n">nominal</span> <span class="o">=</span> <span class="mi">100</span>
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<span class="n">q</span> <span class="o">=</span> <span class="mf">2.84</span> <span class="o">/</span> <span class="mi">100</span>
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<span class="n">σ</span> <span class="o">=</span> <span class="mf">15.37</span> <span class="o">/</span> <span class="mi">100</span>
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<span class="n">term</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">49</span><span class="p">,</span> <span class="o">.</span><span class="mi">9</span><span class="p">,</span> <span class="mf">1.21</span><span class="p">,</span> <span class="mf">1.45</span><span class="p">,</span> <span class="mf">1.69</span><span class="p">]</span> <span class="o">/</span> <span class="mi">100</span> <span class="o">+</span> <span class="mi">1</span>
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<span class="c">###</span>
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<span class="c"># Potential: Based on PEP</span>
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<span class="c"># S0 = 100.6</span>
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<span class="c"># σ = 14.86</span>
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<span class="c"># q = 2.7</span>
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<span class="c">###</span>
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<span class="c"># Simulation parameters</span>
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<span class="n">T</span> <span class="o">=</span> <span class="mi">5</span> <span class="c"># Using years as the unit of time</span>
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<span class="n">n</span> <span class="o">=</span> <span class="mi">250</span> <span class="c"># simulations per year</span>
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<span class="n">m</span> <span class="o">=</span> <span class="mi">100000</span> <span class="c"># paths</span>
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<span class="n">num_simulations</span> <span class="o">=</span> <span class="mi">5</span><span class="p">;</span> <span class="c"># simulation rounds per price</span>
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</pre></div>
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<div class="output_area"><div class="prompt output_prompt">Out[2]:</div>
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<pre>5</pre>
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<h3 id="Defining-the-simulation">Defining the simulation<a class="anchor-link" href="#Defining-the-simulation">¶</a></h3><p>To make things simpler, we simulate a single year at a time. This allows us to easily add in a dividend policy without too much difficulty, and update the simulation every year to match the term structure. The underlying uses GBM for simulation between years.</p>
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<div class=" highlight hl-julia"><pre><span class="n">simulate_gbm</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">μ</span><span class="p">,</span> <span class="n">σ</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
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<span class="c"># Set the initial state</span>
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<span class="n">m</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">S0</span><span class="p">)</span>
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<span class="n">t</span> <span class="o">=</span> <span class="n">T</span> <span class="o">/</span> <span class="n">n</span>
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<span class="n">motion</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
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<span class="n">motion</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">S0</span>
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<span class="c"># Build out all states</span>
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<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">motion</span><span class="p">[:,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">motion</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">.*</span> <span class="n">exp</span><span class="p">((</span><span class="n">μ</span> <span class="o">-</span> <span class="n">σ</span><span class="o">^</span><span class="mi">2</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">t</span><span class="p">)</span> <span class="o">.*</span> <span class="n">exp</span><span class="p">(</span><span class="n">sqrt</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">σ</span> <span class="o">.*</span> <span class="n">randn</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
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<span class="k">end</span>
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<span class="k">return</span> <span class="n">motion</span>
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<span class="k">end</span>
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<span class="k">function</span><span class="nf"> display_motion</span><span class="p">(</span><span class="n">motion</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
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<span class="c"># Given a matrix of paths, display the motion</span>
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<span class="n">n</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">motion</span><span class="p">[</span><span class="mi">1</span><span class="p">,:])</span>
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<span class="n">m</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">motion</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">repmat</span><span class="p">(</span><span class="mi">1</span><span class="p">:</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
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<span class="c"># Calculate the ticks we're going to use. We'd like to</span>
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<span class="c"># have an xtick every month, so calculate where those</span>
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<span class="c"># ticks will actually be at.</span>
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<span class="k">if</span> <span class="p">(</span><span class="n">T</span> <span class="o">></span> <span class="mi">3</span><span class="p">)</span>
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<span class="n">num_ticks</span> <span class="o">=</span> <span class="n">T</span>
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<span class="n">xlabel</span> <span class="o">=</span> <span class="s">"Years"</span>
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<span class="k">else</span>
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<span class="n">num_ticks</span> <span class="o">=</span> <span class="n">T</span> <span class="o">*</span> <span class="mi">12</span>
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<span class="n">xlabel</span> <span class="o">=</span> <span class="s">"Months"</span>
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<span class="k">end</span>
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||
<span class="n">tick_width</span> <span class="o">=</span> <span class="n">n</span> <span class="o">/</span> <span class="n">num_ticks</span>
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<span class="n">x_ticks</span> <span class="o">=</span> <span class="p">[]</span>
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||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">round</span><span class="p">(</span><span class="n">num_ticks</span><span class="p">)</span>
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||
<span class="n">x_ticks</span> <span class="o">=</span> <span class="n">vcat</span><span class="p">(</span><span class="n">x_ticks</span><span class="p">,</span> <span class="n">i</span><span class="o">*</span><span class="n">tick_width</span><span class="p">)</span>
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<span class="k">end</span>
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||
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<span class="c"># Use one color for each path. I'm not sure if there's</span>
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||
<span class="c"># a better way to do this without going through DataFrames</span>
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||
<span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
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||
<span class="k">for</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">1</span><span class="p">:</span><span class="n">m</span>
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||
<span class="n">colors</span> <span class="o">=</span> <span class="n">vcat</span><span class="p">(</span><span class="n">colors</span><span class="p">,</span> <span class="n">ones</span><span class="p">(</span><span class="n">n</span><span class="p">)</span><span class="o">*</span><span class="n">i</span><span class="p">)</span>
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||
<span class="k">end</span>
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||
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<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">motion</span><span class="o">'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">Geom</span><span class="o">.</span><span class="n">line</span><span class="p">,</span>
|
||
<span class="n">Guide</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">x_ticks</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">false</span><span class="p">),</span>
|
||
<span class="n">Guide</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">),</span>
|
||
<span class="n">Guide</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">"Value"</span><span class="p">))</span>
|
||
<span class="k">end</span><span class="p">;</span>
|
||
</pre></div>
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<h3 id="Example-simulation">Example simulation<a class="anchor-link" href="#Example-simulation">¶</a></h3><p>Let's go ahead and run a sample simulation to see what the functions got us!</p>
|
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<div class=" highlight hl-julia"><pre><span class="n">initial</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">S0</span>
|
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<span class="c"># Using μ=0, T=.25 for now, we'll use the proper values later</span>
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<span class="n">motion</span> <span class="o">=</span> <span class="n">simulate_gbm</span><span class="p">(</span><span class="n">initial</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">σ</span><span class="p">,</span> <span class="o">.</span><span class="mi">25</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
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<span class="n">display_motion</span><span class="p">(</span><span class="n">motion</span><span class="p">,</span> <span class="o">.</span><span class="mi">25</span><span class="p">)</span>
|
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</pre></div>
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<?xml version="1.0" encoding="UTF-8"?>
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||
return this;
|
||
};
|
||
})(events[i]);
|
||
}
|
||
|
||
|
||
Element.prototype.mousewheel = function (fn, scope) {
|
||
if (Snap.is(fn, "function")) {
|
||
var el = this;
|
||
var fn2 = function (event) {
|
||
fn.apply(el, [event]);
|
||
};
|
||
}
|
||
|
||
this.node.addEventListener(
|
||
/Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel",
|
||
fn2);
|
||
|
||
return this;
|
||
};
|
||
|
||
|
||
// Snap's attr function can be too slow for things like panning/zooming.
|
||
// This is a function to directly update element attributes without going
|
||
// through eve.
|
||
Element.prototype.attribute = function(key, val) {
|
||
if (val === undefined) {
|
||
return this.node.getAttribute(key);
|
||
} else {
|
||
this.node.setAttribute(key, val);
|
||
return this;
|
||
}
|
||
};
|
||
|
||
Element.prototype.init_gadfly = function() {
|
||
this.mouseenter(Gadfly.plot_mouseover)
|
||
.mouseleave(Gadfly.plot_mouseout)
|
||
.dblclick(Gadfly.plot_dblclick)
|
||
.mousewheel(Gadfly.guide_background_scroll)
|
||
.drag(Gadfly.guide_background_drag_onmove,
|
||
Gadfly.guide_background_drag_onstart,
|
||
Gadfly.guide_background_drag_onend);
|
||
this.mouseenter(function (event) {
|
||
init_pan_zoom(this.plotroot());
|
||
});
|
||
return this;
|
||
};
|
||
});
|
||
|
||
|
||
// When the plot is moused over, emphasize the grid lines.
|
||
Gadfly.plot_mouseover = function(event) {
|
||
var root = this.plotroot();
|
||
|
||
var keyboard_zoom = function(event) {
|
||
if (event.which == 187) { // plus
|
||
increase_zoom_by_position(root, 0.1, true);
|
||
} else if (event.which == 189) { // minus
|
||
increase_zoom_by_position(root, -0.1, true);
|
||
}
|
||
};
|
||
root.data("keyboard_zoom", keyboard_zoom);
|
||
window.addEventListener("keyup", keyboard_zoom);
|
||
|
||
var xgridlines = root.select(".xgridlines"),
|
||
ygridlines = root.select(".ygridlines");
|
||
|
||
xgridlines.data("unfocused_strokedash",
|
||
xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
|
||
ygridlines.data("unfocused_strokedash",
|
||
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
|
||
|
||
// emphasize grid lines
|
||
var destcolor = root.data("focused_xgrid_color");
|
||
xgridlines.attribute("stroke-dasharray", "none")
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
destcolor = root.data("focused_ygrid_color");
|
||
ygridlines.attribute("stroke-dasharray", "none")
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
// reveal zoom slider
|
||
root.select(".zoomslider")
|
||
.animate({opacity: 1.0}, 250);
|
||
};
|
||
|
||
// Reset pan and zoom on double click
|
||
Gadfly.plot_dblclick = function(event) {
|
||
set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);
|
||
};
|
||
|
||
// Unemphasize grid lines on mouse out.
|
||
Gadfly.plot_mouseout = function(event) {
|
||
var root = this.plotroot();
|
||
|
||
window.removeEventListener("keyup", root.data("keyboard_zoom"));
|
||
root.data("keyboard_zoom", undefined);
|
||
|
||
var xgridlines = root.select(".xgridlines"),
|
||
ygridlines = root.select(".ygridlines");
|
||
|
||
var destcolor = root.data("unfocused_xgrid_color");
|
||
|
||
xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash"))
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
destcolor = root.data("unfocused_ygrid_color");
|
||
ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash"))
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
// hide zoom slider
|
||
root.select(".zoomslider")
|
||
.animate({opacity: 0.0}, 250);
|
||
};
|
||
|
||
|
||
var set_geometry_transform = function(root, tx, ty, scale) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
|
||
var old_scale = root.data("scale");
|
||
|
||
var xscale = xscalable ? scale : 1.0,
|
||
yscale = yscalable ? scale : 1.0;
|
||
|
||
tx = xscalable ? tx : 0.0;
|
||
ty = yscalable ? ty : 0.0;
|
||
|
||
var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);
|
||
|
||
root.selectAll(".geometry, image")
|
||
.forEach(function (element, i) {
|
||
element.transform(t);
|
||
});
|
||
|
||
bounds = root.plotbounds();
|
||
|
||
if (yscalable) {
|
||
var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);
|
||
root.selectAll(".xfixed")
|
||
.forEach(function (element, i) {
|
||
element.transform(xfixed_t);
|
||
});
|
||
|
||
root.select(".ylabels")
|
||
.transform(xfixed_t)
|
||
.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var cx = element.asPX("x"),
|
||
cy = element.asPX("y");
|
||
var st = element.data("static_transform");
|
||
unscale_t = new Snap.Matrix();
|
||
unscale_t.scale(1, 1/scale, cx, cy).add(st);
|
||
element.transform(unscale_t);
|
||
|
||
var y = cy * scale + ty;
|
||
element.attr("visibility",
|
||
bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden");
|
||
}
|
||
});
|
||
}
|
||
|
||
if (xscalable) {
|
||
var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);
|
||
var xtrans = new Snap.Matrix().translate(tx, 0);
|
||
root.selectAll(".yfixed")
|
||
.forEach(function (element, i) {
|
||
element.transform(yfixed_t);
|
||
});
|
||
|
||
root.select(".xlabels")
|
||
.transform(yfixed_t)
|
||
.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var cx = element.asPX("x"),
|
||
cy = element.asPX("y");
|
||
var st = element.data("static_transform");
|
||
unscale_t = new Snap.Matrix();
|
||
unscale_t.scale(1/scale, 1, cx, cy).add(st);
|
||
|
||
element.transform(unscale_t);
|
||
|
||
var x = cx * scale + tx;
|
||
element.attr("visibility",
|
||
bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden");
|
||
}
|
||
});
|
||
}
|
||
|
||
// we must unscale anything that is scale invariance: widths, raiduses, etc.
|
||
var size_attribs = ["font-size"];
|
||
var unscaled_selection = ".geometry, .geometry *";
|
||
if (xscalable) {
|
||
size_attribs.push("rx");
|
||
unscaled_selection += ", .xgridlines";
|
||
}
|
||
if (yscalable) {
|
||
size_attribs.push("ry");
|
||
unscaled_selection += ", .ygridlines";
|
||
}
|
||
|
||
root.selectAll(unscaled_selection)
|
||
.forEach(function (element, i) {
|
||
// circle need special help
|
||
if (element.node.nodeName == "circle") {
|
||
var cx = element.attribute("cx"),
|
||
cy = element.attribute("cy");
|
||
unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,
|
||
cx, cy);
|
||
element.transform(unscale_t);
|
||
return;
|
||
}
|
||
|
||
for (i in size_attribs) {
|
||
var key = size_attribs[i];
|
||
var val = parseFloat(element.attribute(key));
|
||
if (val !== undefined && val != 0 && !isNaN(val)) {
|
||
element.attribute(key, val * old_scale / scale);
|
||
}
|
||
}
|
||
});
|
||
};
|
||
|
||
|
||
// Find the most appropriate tick scale and update label visibility.
|
||
var update_tickscale = function(root, scale, axis) {
|
||
if (!root.hasClass(axis + "scalable")) return;
|
||
|
||
var tickscales = root.data(axis + "tickscales");
|
||
var best_tickscale = 1.0;
|
||
var best_tickscale_dist = Infinity;
|
||
for (tickscale in tickscales) {
|
||
var dist = Math.abs(Math.log(tickscale) - Math.log(scale));
|
||
if (dist < best_tickscale_dist) {
|
||
best_tickscale_dist = dist;
|
||
best_tickscale = tickscale;
|
||
}
|
||
}
|
||
|
||
if (best_tickscale != root.data(axis + "tickscale")) {
|
||
root.data(axis + "tickscale", best_tickscale);
|
||
var mark_inscale_gridlines = function (element, i) {
|
||
var inscale = element.attr("gadfly:scale") == best_tickscale;
|
||
element.attribute("gadfly:inscale", inscale);
|
||
element.attr("visibility", inscale ? "visible" : "hidden");
|
||
};
|
||
|
||
var mark_inscale_labels = function (element, i) {
|
||
var inscale = element.attr("gadfly:scale") == best_tickscale;
|
||
element.attribute("gadfly:inscale", inscale);
|
||
element.attr("visibility", inscale ? "visible" : "hidden");
|
||
};
|
||
|
||
root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines);
|
||
root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels);
|
||
}
|
||
};
|
||
|
||
|
||
var set_plot_pan_zoom = function(root, tx, ty, scale) {
|
||
var old_scale = root.data("scale");
|
||
var bounds = root.plotbounds();
|
||
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
|
||
// compute the viewport derived from tx, ty, and scale
|
||
var x_min = -width * scale - (scale * width - width),
|
||
x_max = width * scale,
|
||
y_min = -height * scale - (scale * height - height),
|
||
y_max = height * scale;
|
||
|
||
var x0 = bounds.x0 - scale * bounds.x0,
|
||
y0 = bounds.y0 - scale * bounds.y0;
|
||
|
||
var tx = Math.max(Math.min(tx - x0, x_max), x_min),
|
||
ty = Math.max(Math.min(ty - y0, y_max), y_min);
|
||
|
||
tx += x0;
|
||
ty += y0;
|
||
|
||
// when the scale change, we may need to alter which set of
|
||
// ticks is being displayed
|
||
if (scale != old_scale) {
|
||
update_tickscale(root, scale, "x");
|
||
update_tickscale(root, scale, "y");
|
||
}
|
||
|
||
set_geometry_transform(root, tx, ty, scale);
|
||
|
||
root.data("scale", scale);
|
||
root.data("tx", tx);
|
||
root.data("ty", ty);
|
||
};
|
||
|
||
|
||
var scale_centered_translation = function(root, scale) {
|
||
var bounds = root.plotbounds();
|
||
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
|
||
var tx0 = root.data("tx"),
|
||
ty0 = root.data("ty");
|
||
|
||
var scale0 = root.data("scale");
|
||
|
||
// how off from center the current view is
|
||
var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),
|
||
yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);
|
||
|
||
// rescale offsets
|
||
xoff = xoff * scale / scale0;
|
||
yoff = yoff * scale / scale0;
|
||
|
||
// adjust for the panel position being scaled
|
||
var x_edge_adjust = bounds.x0 * (1 - scale),
|
||
y_edge_adjust = bounds.y0 * (1 - scale);
|
||
|
||
return {
|
||
x: xoff + x_edge_adjust + (width - width * scale) / 2,
|
||
y: yoff + y_edge_adjust + (height - height * scale) / 2
|
||
};
|
||
};
|
||
|
||
|
||
// Initialize data for panning zooming if it isn't already.
|
||
var init_pan_zoom = function(root) {
|
||
if (root.data("zoompan-ready")) {
|
||
return;
|
||
}
|
||
|
||
// The non-scaling-stroke trick. Rather than try to correct for the
|
||
// stroke-width when zooming, we force it to a fixed value.
|
||
var px_per_mm = root.node.getCTM().a;
|
||
|
||
// Drag events report deltas in pixels, which we'd like to convert to
|
||
// millimeters.
|
||
root.data("px_per_mm", px_per_mm);
|
||
|
||
root.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
sw = element.asPX("stroke-width") * px_per_mm;
|
||
if (sw > 0) {
|
||
element.attribute("stroke-width", sw);
|
||
element.attribute("vector-effect", "non-scaling-stroke");
|
||
}
|
||
});
|
||
|
||
// Store ticks labels original tranformation
|
||
root.selectAll(".xlabels > text, .ylabels > text")
|
||
.forEach(function (element, i) {
|
||
var lm = element.transform().localMatrix;
|
||
element.data("static_transform",
|
||
new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));
|
||
});
|
||
|
||
var xgridlines = root.select(".xgridlines");
|
||
var ygridlines = root.select(".ygridlines");
|
||
var xlabels = root.select(".xlabels");
|
||
var ylabels = root.select(".ylabels");
|
||
|
||
if (root.data("tx") === undefined) root.data("tx", 0);
|
||
if (root.data("ty") === undefined) root.data("ty", 0);
|
||
if (root.data("scale") === undefined) root.data("scale", 1.0);
|
||
if (root.data("xtickscales") === undefined) {
|
||
|
||
// index all the tick scales that are listed
|
||
var xtickscales = {};
|
||
var ytickscales = {};
|
||
var add_x_tick_scales = function (element, i) {
|
||
xtickscales[element.attribute("gadfly:scale")] = true;
|
||
};
|
||
var add_y_tick_scales = function (element, i) {
|
||
ytickscales[element.attribute("gadfly:scale")] = true;
|
||
};
|
||
|
||
if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales);
|
||
if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales);
|
||
if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales);
|
||
if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales);
|
||
|
||
root.data("xtickscales", xtickscales);
|
||
root.data("ytickscales", ytickscales);
|
||
root.data("xtickscale", 1.0);
|
||
}
|
||
|
||
var min_scale = 1.0, max_scale = 1.0;
|
||
for (scale in xtickscales) {
|
||
min_scale = Math.min(min_scale, scale);
|
||
max_scale = Math.max(max_scale, scale);
|
||
}
|
||
for (scale in ytickscales) {
|
||
min_scale = Math.min(min_scale, scale);
|
||
max_scale = Math.max(max_scale, scale);
|
||
}
|
||
root.data("min_scale", min_scale);
|
||
root.data("max_scale", max_scale);
|
||
|
||
// store the original positions of labels
|
||
if (xlabels) {
|
||
xlabels.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
element.data("x", element.asPX("x"));
|
||
});
|
||
}
|
||
|
||
if (ylabels) {
|
||
ylabels.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
element.data("y", element.asPX("y"));
|
||
});
|
||
}
|
||
|
||
// mark grid lines and ticks as in or out of scale.
|
||
var mark_inscale = function (element, i) {
|
||
element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0);
|
||
};
|
||
|
||
if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale);
|
||
if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale);
|
||
if (xlabels) xlabels.selectAll("text").forEach(mark_inscale);
|
||
if (ylabels) ylabels.selectAll("text").forEach(mark_inscale);
|
||
|
||
// figure out the upper ond lower bounds on panning using the maximum
|
||
// and minum grid lines
|
||
var bounds = root.plotbounds();
|
||
var pan_bounds = {
|
||
x0: 0.0,
|
||
y0: 0.0,
|
||
x1: 0.0,
|
||
y1: 0.0
|
||
};
|
||
|
||
if (xgridlines) {
|
||
xgridlines
|
||
.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var bbox = element.node.getBBox();
|
||
if (bounds.x1 - bbox.x < pan_bounds.x0) {
|
||
pan_bounds.x0 = bounds.x1 - bbox.x;
|
||
}
|
||
if (bounds.x0 - bbox.x > pan_bounds.x1) {
|
||
pan_bounds.x1 = bounds.x0 - bbox.x;
|
||
}
|
||
element.attr("visibility", "visible");
|
||
}
|
||
});
|
||
}
|
||
|
||
if (ygridlines) {
|
||
ygridlines
|
||
.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var bbox = element.node.getBBox();
|
||
if (bounds.y1 - bbox.y < pan_bounds.y0) {
|
||
pan_bounds.y0 = bounds.y1 - bbox.y;
|
||
}
|
||
if (bounds.y0 - bbox.y > pan_bounds.y1) {
|
||
pan_bounds.y1 = bounds.y0 - bbox.y;
|
||
}
|
||
element.attr("visibility", "visible");
|
||
}
|
||
});
|
||
}
|
||
|
||
// nudge these values a little
|
||
pan_bounds.x0 -= 5;
|
||
pan_bounds.x1 += 5;
|
||
pan_bounds.y0 -= 5;
|
||
pan_bounds.y1 += 5;
|
||
root.data("pan_bounds", pan_bounds);
|
||
|
||
root.data("zoompan-ready", true)
|
||
};
|
||
|
||
|
||
// drag actions, i.e. zooming and panning
|
||
var pan_action = {
|
||
start: function(root, x, y, event) {
|
||
root.data("dx", 0);
|
||
root.data("dy", 0);
|
||
root.data("tx0", root.data("tx"));
|
||
root.data("ty0", root.data("ty"));
|
||
},
|
||
update: function(root, dx, dy, x, y, event) {
|
||
var px_per_mm = root.data("px_per_mm");
|
||
dx /= px_per_mm;
|
||
dy /= px_per_mm;
|
||
|
||
var tx0 = root.data("tx"),
|
||
ty0 = root.data("ty");
|
||
|
||
var dx0 = root.data("dx"),
|
||
dy0 = root.data("dy");
|
||
|
||
root.data("dx", dx);
|
||
root.data("dy", dy);
|
||
|
||
dx = dx - dx0;
|
||
dy = dy - dy0;
|
||
|
||
var tx = tx0 + dx,
|
||
ty = ty0 + dy;
|
||
|
||
set_plot_pan_zoom(root, tx, ty, root.data("scale"));
|
||
},
|
||
end: function(root, event) {
|
||
|
||
},
|
||
cancel: function(root) {
|
||
set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale"));
|
||
}
|
||
};
|
||
|
||
var zoom_box;
|
||
var zoom_action = {
|
||
start: function(root, x, y, event) {
|
||
var bounds = root.plotbounds();
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
var ratio = width / height;
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var px_per_mm = root.data("px_per_mm");
|
||
x = xscalable ? x / px_per_mm : bounds.x0;
|
||
y = yscalable ? y / px_per_mm : bounds.y0;
|
||
var w = xscalable ? 0 : width;
|
||
var h = yscalable ? 0 : height;
|
||
zoom_box = root.rect(x, y, w, h).attr({
|
||
"fill": "#000",
|
||
"opacity": 0.25
|
||
});
|
||
zoom_box.data("ratio", ratio);
|
||
},
|
||
update: function(root, dx, dy, x, y, event) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var px_per_mm = root.data("px_per_mm");
|
||
var bounds = root.plotbounds();
|
||
if (yscalable) {
|
||
y /= px_per_mm;
|
||
y = Math.max(bounds.y0, y);
|
||
y = Math.min(bounds.y1, y);
|
||
} else {
|
||
y = bounds.y1;
|
||
}
|
||
if (xscalable) {
|
||
x /= px_per_mm;
|
||
x = Math.max(bounds.x0, x);
|
||
x = Math.min(bounds.x1, x);
|
||
} else {
|
||
x = bounds.x1;
|
||
}
|
||
|
||
dx = x - zoom_box.attr("x");
|
||
dy = y - zoom_box.attr("y");
|
||
if (xscalable && yscalable) {
|
||
var ratio = zoom_box.data("ratio");
|
||
var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));
|
||
var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);
|
||
dx = width * dx / Math.abs(dx);
|
||
dy = height * dy / Math.abs(dy);
|
||
}
|
||
var xoffset = 0,
|
||
yoffset = 0;
|
||
if (dx < 0) {
|
||
xoffset = dx;
|
||
dx = -1 * dx;
|
||
}
|
||
if (dy < 0) {
|
||
yoffset = dy;
|
||
dy = -1 * dy;
|
||
}
|
||
if (isNaN(dy)) {
|
||
dy = 0.0;
|
||
}
|
||
if (isNaN(dx)) {
|
||
dx = 0.0;
|
||
}
|
||
zoom_box.transform("T" + xoffset + "," + yoffset);
|
||
zoom_box.attr("width", dx);
|
||
zoom_box.attr("height", dy);
|
||
},
|
||
end: function(root, event) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var zoom_bounds = zoom_box.getBBox();
|
||
if (zoom_bounds.width * zoom_bounds.height <= 0) {
|
||
return;
|
||
}
|
||
var plot_bounds = root.plotbounds();
|
||
var zoom_factor = 1.0;
|
||
if (yscalable) {
|
||
zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;
|
||
} else {
|
||
zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;
|
||
}
|
||
var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,
|
||
ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;
|
||
set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor);
|
||
zoom_box.remove();
|
||
},
|
||
cancel: function(root) {
|
||
zoom_box.remove();
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onstart = function(x, y, event) {
|
||
var root = this.plotroot();
|
||
var scalable = root.hasClass("xscalable") || root.hasClass("yscalable");
|
||
var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;
|
||
var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;
|
||
var drag_action = zoomable ? zoom_action :
|
||
panable ? pan_action :
|
||
undefined;
|
||
root.data("drag_action", drag_action);
|
||
if (drag_action) {
|
||
var cancel_drag_action = function(event) {
|
||
if (event.which == 27) { // esc key
|
||
drag_action.cancel(root);
|
||
root.data("drag_action", undefined);
|
||
}
|
||
};
|
||
window.addEventListener("keyup", cancel_drag_action);
|
||
root.data("cancel_drag_action", cancel_drag_action);
|
||
drag_action.start(root, x, y, event);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {
|
||
var root = this.plotroot();
|
||
var drag_action = root.data("drag_action");
|
||
if (drag_action) {
|
||
drag_action.update(root, dx, dy, x, y, event);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onend = function(event) {
|
||
var root = this.plotroot();
|
||
window.removeEventListener("keyup", root.data("cancel_drag_action"));
|
||
root.data("cancel_drag_action", undefined);
|
||
var drag_action = root.data("drag_action");
|
||
if (drag_action) {
|
||
drag_action.end(root, event);
|
||
}
|
||
root.data("drag_action", undefined);
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_scroll = function(event) {
|
||
if (event.shiftKey) {
|
||
increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);
|
||
event.preventDefault();
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_button_mouseover = function(event) {
|
||
this.select(".button_logo")
|
||
.animate({fill: this.data("mouseover_color")}, 100);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_button_mouseout = function(event) {
|
||
this.select(".button_logo")
|
||
.animate({fill: this.data("mouseout_color")}, 100);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_zoomout_click = function(event) {
|
||
increase_zoom_by_position(this.plotroot(), -0.1, true);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_zoomin_click = function(event) {
|
||
increase_zoom_by_position(this.plotroot(), 0.1, true);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_track_click = function(event) {
|
||
// TODO
|
||
};
|
||
|
||
|
||
// Map slider position x to scale y using the function y = a*exp(b*x)+c.
|
||
// The constants a, b, and c are solved using the constraint that the function
|
||
// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).
|
||
var scale_from_slider_position = function(position, min_scale, max_scale) {
|
||
var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),
|
||
b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),
|
||
c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);
|
||
return a * Math.exp(b * position) + c;
|
||
}
|
||
|
||
// inverse of scale_from_slider_position
|
||
var slider_position_from_scale = function(scale, min_scale, max_scale) {
|
||
var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),
|
||
b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),
|
||
c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);
|
||
return 1 / b * Math.log((scale - c) / a);
|
||
}
|
||
|
||
var increase_zoom_by_position = function(root, delta_position, animate) {
|
||
var scale = root.data("scale"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale");
|
||
var position = slider_position_from_scale(scale, min_scale, max_scale);
|
||
position += delta_position;
|
||
scale = scale_from_slider_position(position, min_scale, max_scale);
|
||
set_zoom(root, scale, animate);
|
||
}
|
||
|
||
var set_zoom = function(root, scale, animate) {
|
||
var min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale"),
|
||
old_scale = root.data("scale");
|
||
var new_scale = Math.max(min_scale, Math.min(scale, max_scale));
|
||
if (animate) {
|
||
Snap.animate(
|
||
old_scale,
|
||
new_scale,
|
||
function (new_scale) {
|
||
update_plot_scale(root, new_scale);
|
||
},
|
||
200);
|
||
} else {
|
||
update_plot_scale(root, new_scale);
|
||
}
|
||
}
|
||
|
||
|
||
var update_plot_scale = function(root, new_scale) {
|
||
var trans = scale_centered_translation(root, new_scale);
|
||
set_plot_pan_zoom(root, trans.x, trans.y, new_scale);
|
||
|
||
root.selectAll(".zoomslider_thumb")
|
||
.forEach(function (element, i) {
|
||
var min_pos = element.data("min_pos"),
|
||
max_pos = element.data("max_pos"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale");
|
||
var xmid = (min_pos + max_pos) / 2;
|
||
var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);
|
||
element.transform(new Snap.Matrix().translate(
|
||
Math.max(min_pos, Math.min(
|
||
max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));
|
||
});
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {
|
||
var root = this.plotroot();
|
||
var min_pos = this.data("min_pos"),
|
||
max_pos = this.data("max_pos"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale"),
|
||
old_scale = root.data("old_scale");
|
||
|
||
var px_per_mm = root.data("px_per_mm");
|
||
dx /= px_per_mm;
|
||
dy /= px_per_mm;
|
||
|
||
var xmid = (min_pos + max_pos) / 2;
|
||
var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +
|
||
dx / (max_pos - min_pos);
|
||
|
||
// compute the new scale
|
||
var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);
|
||
new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));
|
||
|
||
update_plot_scale(root, new_scale);
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {
|
||
this.animate({fill: this.data("mouseover_color")}, 100);
|
||
var root = this.plotroot();
|
||
|
||
// keep track of what the scale was when we started dragging
|
||
root.data("old_scale", root.data("scale"));
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragend = function(event) {
|
||
this.animate({fill: this.data("mouseout_color")}, 100);
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
var toggle_color_class = function(root, color_class, ison) {
|
||
var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class);
|
||
var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class);
|
||
if (ison) {
|
||
guides.animate({opacity: 0.5}, 250);
|
||
geoms.animate({opacity: 0.0}, 250);
|
||
} else {
|
||
guides.animate({opacity: 1.0}, 250);
|
||
geoms.animate({opacity: 1.0}, 250);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.colorkey_swatch_click = function(event) {
|
||
var root = this.plotroot();
|
||
var color_class = this.data("color_class");
|
||
|
||
if (event.shiftKey) {
|
||
root.selectAll(".colorkey text")
|
||
.forEach(function (element) {
|
||
var other_color_class = element.data("color_class");
|
||
if (other_color_class != color_class) {
|
||
toggle_color_class(root, other_color_class,
|
||
element.attr("opacity") == 1.0);
|
||
}
|
||
});
|
||
} else {
|
||
toggle_color_class(root, color_class, this.attr("opacity") == 1.0);
|
||
}
|
||
};
|
||
|
||
|
||
return Gadfly;
|
||
|
||
}));
|
||
|
||
|
||
//@ sourceURL=gadfly.js
|
||
|
||
(function (glob, factory) {
|
||
// AMD support
|
||
if (typeof require === "function" && typeof define === "function" && define.amd) {
|
||
require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) {
|
||
factory(Snap, Gadfly);
|
||
});
|
||
} else {
|
||
factory(glob.Snap, glob.Gadfly);
|
||
}
|
||
})(window, function (Snap, Gadfly) {
|
||
var fig = Snap("#fig-3a6dd25ad25c4037a166889ee51bb151");
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-8")
|
||
.init_gadfly();
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-11")
|
||
.plotroot().data("unfocused_ygrid_color", "#D0D0E0")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-11")
|
||
.plotroot().data("focused_ygrid_color", "#A0A0A0")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-12")
|
||
.plotroot().data("unfocused_xgrid_color", "#D0D0E0")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-12")
|
||
.plotroot().data("focused_xgrid_color", "#A0A0A0")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-16")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-16")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-16")
|
||
.click(Gadfly.zoomslider_zoomin_click)
|
||
.mouseenter(Gadfly.zoomslider_button_mouseover)
|
||
.mouseleave(Gadfly.zoomslider_button_mouseout)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-18")
|
||
.data("max_pos", 111.58)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-18")
|
||
.data("min_pos", 94.58)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-18")
|
||
.click(Gadfly.zoomslider_track_click);
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-19")
|
||
.data("max_pos", 111.58)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-19")
|
||
.data("min_pos", 94.58)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-19")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-19")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-19")
|
||
.drag(Gadfly.zoomslider_thumb_dragmove,
|
||
Gadfly.zoomslider_thumb_dragstart,
|
||
Gadfly.zoomslider_thumb_dragend)
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
|
||
.click(Gadfly.zoomslider_zoomout_click)
|
||
.mouseenter(Gadfly.zoomslider_button_mouseover)
|
||
.mouseleave(Gadfly.zoomslider_button_mouseout)
|
||
;
|
||
});
|
||
]]> </script>
|
||
</svg>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="cell border-box-sizing text_cell rendered">
|
||
<div class="prompt input_prompt">
|
||
</div>
|
||
<div class="inner_cell">
|
||
<div class="text_cell_render border-box-sizing rendered_html">
|
||
<h3 id="Computing-the-term-structure">Computing the term structure<a class="anchor-link" href="#Computing-the-term-structure">¶</a></h3><p>Now that we've got the basic motion set up, let's start making things a bit more sophisticated for the model. We're going to assume that the drift of the stock is the difference between the implied forward rate and the quarterly dividend rate.</p>
|
||
<p>We're given the yearly term structure, and need to calculate the quarterly forward rate to match this structure. The term structure is assumed to follow:</p>
|
||
<p>$d(0, t) = d(0,t-1)\cdot f_{i-1, i}$</p>
|
||
<p>Where $f_{i-1, i}$ is the quarterly forward rate.</p>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [5]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">forward_term</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">yearly_term</span><span class="p">)</span>
|
||
<span class="c"># It is assumed that we have a yearly term structure passed in, and starts at year 0</span>
|
||
<span class="c"># This implies a nominal rate above 0 for the first year!</span>
|
||
<span class="n">years</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">term</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span> <span class="c"># because we start at 0</span>
|
||
<span class="n">structure</span> <span class="o">=</span> <span class="p">[(</span><span class="n">term</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">term</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">years</span><span class="p">]</span>
|
||
<span class="k">end</span><span class="p">;</span>
|
||
</pre></div>
|
||
|
||
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|
||
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|
||
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|
||
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
<h3 id="Illustrating-the-term-structure">Illustrating the term structure<a class="anchor-link" href="#Illustrating-the-term-structure">¶</a></h3><p>Now that we've got our term structure, let's validate that we're getting the correct results! If we've done this correctly, then:</p>
|
||
|
||
<pre><code>term[2] == term[1] * structure[1]</code></pre>
|
||
|
||
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|
||
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|
||
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|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
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|
||
<div class="prompt input_prompt">In [6]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="c"># Example term structure taken from:</span>
|
||
<span class="c"># http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield</span>
|
||
<span class="c"># Linear interpolation used years in-between periods, assuming real-dollar</span>
|
||
<span class="c"># interest rates</span>
|
||
<span class="n">forward_yield</span> <span class="o">=</span> <span class="n">forward_term</span><span class="p">(</span><span class="n">term</span><span class="p">)</span>
|
||
<span class="n">calculated_term2</span> <span class="o">=</span> <span class="n">term</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">forward_yield</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||
|
||
<span class="n">println</span><span class="p">(</span><span class="s">"Actual term[2]: </span><span class="si">$</span><span class="s">(term[2]); Calculated term[2]: </span><span class="si">$(calculated_term2)</span><span class="s">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
|
||
|
||
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|
||
<div class="output_subarea output_stream output_stdout output_text">
|
||
<pre>Actual term[2]: 1.0049; Calculated term[2]: 1.0049
|
||
</pre>
|
||
</div>
|
||
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|
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|
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|
||
<h3 id="The-full-underlying-simulation">The full underlying simulation<a class="anchor-link" href="#The-full-underlying-simulation">¶</a></h3><p>Now that we have the term structure set up, we can actually start doing some real simulation! Let's construct some paths through the full 5-year time frame. In order to do this, we will simulate 1 year at a time, and use the forward rates at those times to compute the drift. Thus, there will be 5 total simulations batched together.</p>
|
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|
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|
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||
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|
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<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [7]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">full_motion</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">S0</span>
|
||
<span class="n">full_term</span> <span class="o">=</span> <span class="n">vcat</span><span class="p">(</span><span class="n">term</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">forward_yield</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">T</span>
|
||
<span class="n">μ</span> <span class="o">=</span> <span class="p">(</span><span class="n">full_term</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">q</span><span class="p">)</span>
|
||
<span class="n">year_motion</span> <span class="o">=</span> <span class="n">simulate_gbm</span><span class="p">(</span><span class="n">full_motion</span><span class="p">[:,</span><span class="k">end</span><span class="p">],</span> <span class="n">μ</span><span class="p">,</span> <span class="n">σ</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
|
||
<span class="n">full_motion</span> <span class="o">=</span> <span class="n">hcat</span><span class="p">(</span><span class="n">full_motion</span><span class="p">,</span> <span class="n">year_motion</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">display_motion</span><span class="p">(</span><span class="n">full_motion</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
</div>
|
||
</div>
|
||
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|
||
|
||
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<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
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p(d[1]);b.shear/=b.scaley;0>this.determinant()&&(b.scalex=-b.scalex);var e=-d[0][1],d=d[1][1];0>d?(b.rotate=a.deg(f.acos(d)),0>e&&(b.rotate=360-b.rotate)):b.rotate=a.deg(f.asin(e));b.isSimple=!+b.shear.toFixed(9)&&(b.scalex.toFixed(9)==b.scaley.toFixed(9)||!b.rotate);b.isSuperSimple=!+b.shear.toFixed(9)&&b.scalex.toFixed(9)==b.scaley.toFixed(9)&&!b.rotate;b.noRotation=!+b.shear.toFixed(9)&&!b.rotate;return b};n.toTransformString=function(a){a=a||this.split();if(+a.shear.toFixed(9))return"m"+[this.get(0),
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this.get(1),this.get(2),this.get(3),this.get(4),this.get(5)];a.scalex=+a.scalex.toFixed(4);a.scaley=+a.scaley.toFixed(4);a.rotate=+a.rotate.toFixed(4);return(a.dx||a.dy?"t"+[+a.dx.toFixed(4),+a.dy.toFixed(4)]:"")+(1!=a.scalex||1!=a.scaley?"s"+[a.scalex,a.scaley,0,0]:"")+(a.rotate?"r"+[+a.rotate.toFixed(4),0,0]:"")}})(w.prototype);a.Matrix=w;a.matrix=function(a,d,f,b,k,e){return new w(a,d,f,b,k,e)}});C.plugin(function(a,v,y,M,A){function w(h){return function(d){k.stop();d instanceof A&&1==d.node.childNodes.length&&
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w(r,k,function(){return x.getBBox(1)});if("d"==e||"path"==e)return r=a.path.toCubic(r,k),{from:u(r[0]),to:u(r[1]),f:n(r[0])};if("points"==e)return r=q(r).split(a._.separator),s=q(k).split(a._.separator),{from:r,to:s,f:function(a){return a}};aUnit=r.match(b);s=q(k).match(b);return aUnit&&aUnit==s?{from:parseFloat(r),to:parseFloat(k),f:d(aUnit)}:{from:this.asPX(e),to:this.asPX(e,k),f:z}})});C.plugin(function(a,v,y,C){var A=v.prototype,w="createTouch"in C.doc;v="click dblclick mousedown mousemove mouseout mouseover mouseup touchstart touchmove touchend touchcancel".split(" ");
|
||
var z={mousedown:"touchstart",mousemove:"touchmove",mouseup:"touchend"},d=function(a,b){var d="y"==a?"scrollTop":"scrollLeft",e=b&&b.node?b.node.ownerDocument:C.doc;return e[d in e.documentElement?"documentElement":"body"][d]},f=function(){this.returnValue=!1},n=function(){return this.originalEvent.preventDefault()},u=function(){this.cancelBubble=!0},p=function(){return this.originalEvent.stopPropagation()},b=function(){if(C.doc.addEventListener)return function(a,b,e,f){var k=w&&z[b]?z[b]:b,l=function(k){var l=
|
||
d("y",f),q=d("x",f);if(w&&z.hasOwnProperty(b))for(var r=0,u=k.targetTouches&&k.targetTouches.length;r<u;r++)if(k.targetTouches[r].target==a||a.contains(k.targetTouches[r].target)){u=k;k=k.targetTouches[r];k.originalEvent=u;k.preventDefault=n;k.stopPropagation=p;break}return e.call(f,k,k.clientX+q,k.clientY+l)};b!==k&&a.addEventListener(b,l,!1);a.addEventListener(k,l,!1);return function(){b!==k&&a.removeEventListener(b,l,!1);a.removeEventListener(k,l,!1);return!0}};if(C.doc.attachEvent)return function(a,
|
||
b,e,h){var k=function(a){a=a||h.node.ownerDocument.window.event;var b=d("y",h),k=d("x",h),k=a.clientX+k,b=a.clientY+b;a.preventDefault=a.preventDefault||f;a.stopPropagation=a.stopPropagation||u;return e.call(h,a,k,b)};a.attachEvent("on"+b,k);return function(){a.detachEvent("on"+b,k);return!0}}}(),q=[],e=function(a){for(var b=a.clientX,e=a.clientY,f=d("y"),l=d("x"),n,p=q.length;p--;){n=q[p];if(w)for(var r=a.touches&&a.touches.length,u;r--;){if(u=a.touches[r],u.identifier==n.el._drag.id||n.el.node.contains(u.target)){b=
|
||
u.clientX;e=u.clientY;(a.originalEvent?a.originalEvent:a).preventDefault();break}}else a.preventDefault();b+=l;e+=f;k("snap.drag.move."+n.el.id,n.move_scope||n.el,b-n.el._drag.x,e-n.el._drag.y,b,e,a)}},l=function(b){a.unmousemove(e).unmouseup(l);for(var d=q.length,f;d--;)f=q[d],f.el._drag={},k("snap.drag.end."+f.el.id,f.end_scope||f.start_scope||f.move_scope||f.el,b);q=[]};for(y=v.length;y--;)(function(d){a[d]=A[d]=function(e,f){a.is(e,"function")&&(this.events=this.events||[],this.events.push({name:d,
|
||
f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a["un"+d]=A["un"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;
|
||
this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on("snap.drag.start."+this.id,d);b&&k.on("snap.drag.move."+this.id,b);f&&k.on("snap.drag.end."+this.id,f);k("snap.drag.start."+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?"T":"t")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};
|
||
A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind("snap.drag.*."+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\s*url\((.+)\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;"svg"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z("filter");z(u,{id:k,filterUnits:"userSpaceOnUse"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on("snap.util.getattr.filter",
|
||
function(){k.stop();var d=z(this.node,"filter");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on("snap.util.attr.filter",function(d){if(d instanceof v&&"filter"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&"none"!=d||(k.stop(),this.node.removeAttribute("filter"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation="{def}"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=
|
||
function(d,f,k,u,p){"string"==typeof k&&(p=u=k,k=4);"string"!=typeof u&&(p=u,u="#000");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||"#000");return a.format('<feGaussianBlur in="SourceAlpha" stdDeviation="{blur}"/><feOffset dx="{dx}" dy="{dy}" result="offsetblur"/><feFlood flood-color="{color}"/><feComposite in2="offsetblur" operator="in"/><feComponentTransfer><feFuncA type="linear" slope="{opacity}"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in="SourceGraphic"/></feMerge>',
|
||
{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type="matrix" values="{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=
|
||
function(d){null==d&&(d=1);return a.format('<feColorMatrix type="matrix" values="{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type="saturate" values="{amount}"/>',{amount:1-
|
||
d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type="hueRotate" values="{angle}"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="table" tableValues="{amount} {amount2}"/><feFuncG type="table" tableValues="{amount} {amount2}"/><feFuncB type="table" tableValues="{amount} {amount2}"/></feComponentTransfer>',{amount:d,
|
||
amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="linear" slope="{amount}"/><feFuncG type="linear" slope="{amount}"/><feFuncB type="linear" slope="{amount}"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type="linear" slope="{amount}" intercept="{amount2}"/><feFuncG type="linear" slope="{amount}" intercept="{amount2}"/><feFuncB type="linear" slope="{amount}" intercept="{amount2}"/></feComponentTransfer>',
|
||
{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});
|
||
|
||
]]> </script>
|
||
<script> <![CDATA[
|
||
|
||
(function (glob, factory) {
|
||
// AMD support
|
||
if (typeof define === "function" && define.amd) {
|
||
// Define as an anonymous module
|
||
define("Gadfly", ["Snap.svg"], function (Snap) {
|
||
return factory(Snap);
|
||
});
|
||
} else {
|
||
// Browser globals (glob is window)
|
||
// Snap adds itself to window
|
||
glob.Gadfly = factory(glob.Snap);
|
||
}
|
||
}(this, function (Snap) {
|
||
|
||
var Gadfly = {};
|
||
|
||
// Get an x/y coordinate value in pixels
|
||
var xPX = function(fig, x) {
|
||
var client_box = fig.node.getBoundingClientRect();
|
||
return x * fig.node.viewBox.baseVal.width / client_box.width;
|
||
};
|
||
|
||
var yPX = function(fig, y) {
|
||
var client_box = fig.node.getBoundingClientRect();
|
||
return y * fig.node.viewBox.baseVal.height / client_box.height;
|
||
};
|
||
|
||
|
||
Snap.plugin(function (Snap, Element, Paper, global) {
|
||
// Traverse upwards from a snap element to find and return the first
|
||
// note with the "plotroot" class.
|
||
Element.prototype.plotroot = function () {
|
||
var element = this;
|
||
while (!element.hasClass("plotroot") && element.parent() != null) {
|
||
element = element.parent();
|
||
}
|
||
return element;
|
||
};
|
||
|
||
Element.prototype.svgroot = function () {
|
||
var element = this;
|
||
while (element.node.nodeName != "svg" && element.parent() != null) {
|
||
element = element.parent();
|
||
}
|
||
return element;
|
||
};
|
||
|
||
Element.prototype.plotbounds = function () {
|
||
var root = this.plotroot()
|
||
var bbox = root.select(".guide.background").node.getBBox();
|
||
return {
|
||
x0: bbox.x,
|
||
x1: bbox.x + bbox.width,
|
||
y0: bbox.y,
|
||
y1: bbox.y + bbox.height
|
||
};
|
||
};
|
||
|
||
Element.prototype.plotcenter = function () {
|
||
var root = this.plotroot()
|
||
var bbox = root.select(".guide.background").node.getBBox();
|
||
return {
|
||
x: bbox.x + bbox.width / 2,
|
||
y: bbox.y + bbox.height / 2
|
||
};
|
||
};
|
||
|
||
// Emulate IE style mouseenter/mouseleave events, since Microsoft always
|
||
// does everything right.
|
||
// See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/
|
||
var events = ["mouseenter", "mouseleave"];
|
||
|
||
for (i in events) {
|
||
(function (event_name) {
|
||
var event_name = events[i];
|
||
Element.prototype[event_name] = function (fn, scope) {
|
||
if (Snap.is(fn, "function")) {
|
||
var fn2 = function (event) {
|
||
if (event.type != "mouseover" && event.type != "mouseout") {
|
||
return;
|
||
}
|
||
|
||
var reltg = event.relatedTarget ? event.relatedTarget :
|
||
event.type == "mouseout" ? event.toElement : event.fromElement;
|
||
while (reltg && reltg != this.node) reltg = reltg.parentNode;
|
||
|
||
if (reltg != this.node) {
|
||
return fn.apply(this, event);
|
||
}
|
||
};
|
||
|
||
if (event_name == "mouseenter") {
|
||
this.mouseover(fn2, scope);
|
||
} else {
|
||
this.mouseout(fn2, scope);
|
||
}
|
||
}
|
||
return this;
|
||
};
|
||
})(events[i]);
|
||
}
|
||
|
||
|
||
Element.prototype.mousewheel = function (fn, scope) {
|
||
if (Snap.is(fn, "function")) {
|
||
var el = this;
|
||
var fn2 = function (event) {
|
||
fn.apply(el, [event]);
|
||
};
|
||
}
|
||
|
||
this.node.addEventListener(
|
||
/Firefox/i.test(navigator.userAgent) ? "DOMMouseScroll" : "mousewheel",
|
||
fn2);
|
||
|
||
return this;
|
||
};
|
||
|
||
|
||
// Snap's attr function can be too slow for things like panning/zooming.
|
||
// This is a function to directly update element attributes without going
|
||
// through eve.
|
||
Element.prototype.attribute = function(key, val) {
|
||
if (val === undefined) {
|
||
return this.node.getAttribute(key);
|
||
} else {
|
||
this.node.setAttribute(key, val);
|
||
return this;
|
||
}
|
||
};
|
||
|
||
Element.prototype.init_gadfly = function() {
|
||
this.mouseenter(Gadfly.plot_mouseover)
|
||
.mouseleave(Gadfly.plot_mouseout)
|
||
.dblclick(Gadfly.plot_dblclick)
|
||
.mousewheel(Gadfly.guide_background_scroll)
|
||
.drag(Gadfly.guide_background_drag_onmove,
|
||
Gadfly.guide_background_drag_onstart,
|
||
Gadfly.guide_background_drag_onend);
|
||
this.mouseenter(function (event) {
|
||
init_pan_zoom(this.plotroot());
|
||
});
|
||
return this;
|
||
};
|
||
});
|
||
|
||
|
||
// When the plot is moused over, emphasize the grid lines.
|
||
Gadfly.plot_mouseover = function(event) {
|
||
var root = this.plotroot();
|
||
|
||
var keyboard_zoom = function(event) {
|
||
if (event.which == 187) { // plus
|
||
increase_zoom_by_position(root, 0.1, true);
|
||
} else if (event.which == 189) { // minus
|
||
increase_zoom_by_position(root, -0.1, true);
|
||
}
|
||
};
|
||
root.data("keyboard_zoom", keyboard_zoom);
|
||
window.addEventListener("keyup", keyboard_zoom);
|
||
|
||
var xgridlines = root.select(".xgridlines"),
|
||
ygridlines = root.select(".ygridlines");
|
||
|
||
xgridlines.data("unfocused_strokedash",
|
||
xgridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
|
||
ygridlines.data("unfocused_strokedash",
|
||
ygridlines.attribute("stroke-dasharray").replace(/(\d)(,|$)/g, "$1mm$2"));
|
||
|
||
// emphasize grid lines
|
||
var destcolor = root.data("focused_xgrid_color");
|
||
xgridlines.attribute("stroke-dasharray", "none")
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
destcolor = root.data("focused_ygrid_color");
|
||
ygridlines.attribute("stroke-dasharray", "none")
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
// reveal zoom slider
|
||
root.select(".zoomslider")
|
||
.animate({opacity: 1.0}, 250);
|
||
};
|
||
|
||
// Reset pan and zoom on double click
|
||
Gadfly.plot_dblclick = function(event) {
|
||
set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);
|
||
};
|
||
|
||
// Unemphasize grid lines on mouse out.
|
||
Gadfly.plot_mouseout = function(event) {
|
||
var root = this.plotroot();
|
||
|
||
window.removeEventListener("keyup", root.data("keyboard_zoom"));
|
||
root.data("keyboard_zoom", undefined);
|
||
|
||
var xgridlines = root.select(".xgridlines"),
|
||
ygridlines = root.select(".ygridlines");
|
||
|
||
var destcolor = root.data("unfocused_xgrid_color");
|
||
|
||
xgridlines.attribute("stroke-dasharray", xgridlines.data("unfocused_strokedash"))
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
destcolor = root.data("unfocused_ygrid_color");
|
||
ygridlines.attribute("stroke-dasharray", ygridlines.data("unfocused_strokedash"))
|
||
.selectAll("path")
|
||
.animate({stroke: destcolor}, 250);
|
||
|
||
// hide zoom slider
|
||
root.select(".zoomslider")
|
||
.animate({opacity: 0.0}, 250);
|
||
};
|
||
|
||
|
||
var set_geometry_transform = function(root, tx, ty, scale) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
|
||
var old_scale = root.data("scale");
|
||
|
||
var xscale = xscalable ? scale : 1.0,
|
||
yscale = yscalable ? scale : 1.0;
|
||
|
||
tx = xscalable ? tx : 0.0;
|
||
ty = yscalable ? ty : 0.0;
|
||
|
||
var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);
|
||
|
||
root.selectAll(".geometry, image")
|
||
.forEach(function (element, i) {
|
||
element.transform(t);
|
||
});
|
||
|
||
bounds = root.plotbounds();
|
||
|
||
if (yscalable) {
|
||
var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);
|
||
root.selectAll(".xfixed")
|
||
.forEach(function (element, i) {
|
||
element.transform(xfixed_t);
|
||
});
|
||
|
||
root.select(".ylabels")
|
||
.transform(xfixed_t)
|
||
.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var cx = element.asPX("x"),
|
||
cy = element.asPX("y");
|
||
var st = element.data("static_transform");
|
||
unscale_t = new Snap.Matrix();
|
||
unscale_t.scale(1, 1/scale, cx, cy).add(st);
|
||
element.transform(unscale_t);
|
||
|
||
var y = cy * scale + ty;
|
||
element.attr("visibility",
|
||
bounds.y0 <= y && y <= bounds.y1 ? "visible" : "hidden");
|
||
}
|
||
});
|
||
}
|
||
|
||
if (xscalable) {
|
||
var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);
|
||
var xtrans = new Snap.Matrix().translate(tx, 0);
|
||
root.selectAll(".yfixed")
|
||
.forEach(function (element, i) {
|
||
element.transform(yfixed_t);
|
||
});
|
||
|
||
root.select(".xlabels")
|
||
.transform(yfixed_t)
|
||
.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var cx = element.asPX("x"),
|
||
cy = element.asPX("y");
|
||
var st = element.data("static_transform");
|
||
unscale_t = new Snap.Matrix();
|
||
unscale_t.scale(1/scale, 1, cx, cy).add(st);
|
||
|
||
element.transform(unscale_t);
|
||
|
||
var x = cx * scale + tx;
|
||
element.attr("visibility",
|
||
bounds.x0 <= x && x <= bounds.x1 ? "visible" : "hidden");
|
||
}
|
||
});
|
||
}
|
||
|
||
// we must unscale anything that is scale invariance: widths, raiduses, etc.
|
||
var size_attribs = ["font-size"];
|
||
var unscaled_selection = ".geometry, .geometry *";
|
||
if (xscalable) {
|
||
size_attribs.push("rx");
|
||
unscaled_selection += ", .xgridlines";
|
||
}
|
||
if (yscalable) {
|
||
size_attribs.push("ry");
|
||
unscaled_selection += ", .ygridlines";
|
||
}
|
||
|
||
root.selectAll(unscaled_selection)
|
||
.forEach(function (element, i) {
|
||
// circle need special help
|
||
if (element.node.nodeName == "circle") {
|
||
var cx = element.attribute("cx"),
|
||
cy = element.attribute("cy");
|
||
unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,
|
||
cx, cy);
|
||
element.transform(unscale_t);
|
||
return;
|
||
}
|
||
|
||
for (i in size_attribs) {
|
||
var key = size_attribs[i];
|
||
var val = parseFloat(element.attribute(key));
|
||
if (val !== undefined && val != 0 && !isNaN(val)) {
|
||
element.attribute(key, val * old_scale / scale);
|
||
}
|
||
}
|
||
});
|
||
};
|
||
|
||
|
||
// Find the most appropriate tick scale and update label visibility.
|
||
var update_tickscale = function(root, scale, axis) {
|
||
if (!root.hasClass(axis + "scalable")) return;
|
||
|
||
var tickscales = root.data(axis + "tickscales");
|
||
var best_tickscale = 1.0;
|
||
var best_tickscale_dist = Infinity;
|
||
for (tickscale in tickscales) {
|
||
var dist = Math.abs(Math.log(tickscale) - Math.log(scale));
|
||
if (dist < best_tickscale_dist) {
|
||
best_tickscale_dist = dist;
|
||
best_tickscale = tickscale;
|
||
}
|
||
}
|
||
|
||
if (best_tickscale != root.data(axis + "tickscale")) {
|
||
root.data(axis + "tickscale", best_tickscale);
|
||
var mark_inscale_gridlines = function (element, i) {
|
||
var inscale = element.attr("gadfly:scale") == best_tickscale;
|
||
element.attribute("gadfly:inscale", inscale);
|
||
element.attr("visibility", inscale ? "visible" : "hidden");
|
||
};
|
||
|
||
var mark_inscale_labels = function (element, i) {
|
||
var inscale = element.attr("gadfly:scale") == best_tickscale;
|
||
element.attribute("gadfly:inscale", inscale);
|
||
element.attr("visibility", inscale ? "visible" : "hidden");
|
||
};
|
||
|
||
root.select("." + axis + "gridlines").selectAll("path").forEach(mark_inscale_gridlines);
|
||
root.select("." + axis + "labels").selectAll("text").forEach(mark_inscale_labels);
|
||
}
|
||
};
|
||
|
||
|
||
var set_plot_pan_zoom = function(root, tx, ty, scale) {
|
||
var old_scale = root.data("scale");
|
||
var bounds = root.plotbounds();
|
||
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
|
||
// compute the viewport derived from tx, ty, and scale
|
||
var x_min = -width * scale - (scale * width - width),
|
||
x_max = width * scale,
|
||
y_min = -height * scale - (scale * height - height),
|
||
y_max = height * scale;
|
||
|
||
var x0 = bounds.x0 - scale * bounds.x0,
|
||
y0 = bounds.y0 - scale * bounds.y0;
|
||
|
||
var tx = Math.max(Math.min(tx - x0, x_max), x_min),
|
||
ty = Math.max(Math.min(ty - y0, y_max), y_min);
|
||
|
||
tx += x0;
|
||
ty += y0;
|
||
|
||
// when the scale change, we may need to alter which set of
|
||
// ticks is being displayed
|
||
if (scale != old_scale) {
|
||
update_tickscale(root, scale, "x");
|
||
update_tickscale(root, scale, "y");
|
||
}
|
||
|
||
set_geometry_transform(root, tx, ty, scale);
|
||
|
||
root.data("scale", scale);
|
||
root.data("tx", tx);
|
||
root.data("ty", ty);
|
||
};
|
||
|
||
|
||
var scale_centered_translation = function(root, scale) {
|
||
var bounds = root.plotbounds();
|
||
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
|
||
var tx0 = root.data("tx"),
|
||
ty0 = root.data("ty");
|
||
|
||
var scale0 = root.data("scale");
|
||
|
||
// how off from center the current view is
|
||
var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),
|
||
yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);
|
||
|
||
// rescale offsets
|
||
xoff = xoff * scale / scale0;
|
||
yoff = yoff * scale / scale0;
|
||
|
||
// adjust for the panel position being scaled
|
||
var x_edge_adjust = bounds.x0 * (1 - scale),
|
||
y_edge_adjust = bounds.y0 * (1 - scale);
|
||
|
||
return {
|
||
x: xoff + x_edge_adjust + (width - width * scale) / 2,
|
||
y: yoff + y_edge_adjust + (height - height * scale) / 2
|
||
};
|
||
};
|
||
|
||
|
||
// Initialize data for panning zooming if it isn't already.
|
||
var init_pan_zoom = function(root) {
|
||
if (root.data("zoompan-ready")) {
|
||
return;
|
||
}
|
||
|
||
// The non-scaling-stroke trick. Rather than try to correct for the
|
||
// stroke-width when zooming, we force it to a fixed value.
|
||
var px_per_mm = root.node.getCTM().a;
|
||
|
||
// Drag events report deltas in pixels, which we'd like to convert to
|
||
// millimeters.
|
||
root.data("px_per_mm", px_per_mm);
|
||
|
||
root.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
sw = element.asPX("stroke-width") * px_per_mm;
|
||
if (sw > 0) {
|
||
element.attribute("stroke-width", sw);
|
||
element.attribute("vector-effect", "non-scaling-stroke");
|
||
}
|
||
});
|
||
|
||
// Store ticks labels original tranformation
|
||
root.selectAll(".xlabels > text, .ylabels > text")
|
||
.forEach(function (element, i) {
|
||
var lm = element.transform().localMatrix;
|
||
element.data("static_transform",
|
||
new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));
|
||
});
|
||
|
||
var xgridlines = root.select(".xgridlines");
|
||
var ygridlines = root.select(".ygridlines");
|
||
var xlabels = root.select(".xlabels");
|
||
var ylabels = root.select(".ylabels");
|
||
|
||
if (root.data("tx") === undefined) root.data("tx", 0);
|
||
if (root.data("ty") === undefined) root.data("ty", 0);
|
||
if (root.data("scale") === undefined) root.data("scale", 1.0);
|
||
if (root.data("xtickscales") === undefined) {
|
||
|
||
// index all the tick scales that are listed
|
||
var xtickscales = {};
|
||
var ytickscales = {};
|
||
var add_x_tick_scales = function (element, i) {
|
||
xtickscales[element.attribute("gadfly:scale")] = true;
|
||
};
|
||
var add_y_tick_scales = function (element, i) {
|
||
ytickscales[element.attribute("gadfly:scale")] = true;
|
||
};
|
||
|
||
if (xgridlines) xgridlines.selectAll("path").forEach(add_x_tick_scales);
|
||
if (ygridlines) ygridlines.selectAll("path").forEach(add_y_tick_scales);
|
||
if (xlabels) xlabels.selectAll("text").forEach(add_x_tick_scales);
|
||
if (ylabels) ylabels.selectAll("text").forEach(add_y_tick_scales);
|
||
|
||
root.data("xtickscales", xtickscales);
|
||
root.data("ytickscales", ytickscales);
|
||
root.data("xtickscale", 1.0);
|
||
}
|
||
|
||
var min_scale = 1.0, max_scale = 1.0;
|
||
for (scale in xtickscales) {
|
||
min_scale = Math.min(min_scale, scale);
|
||
max_scale = Math.max(max_scale, scale);
|
||
}
|
||
for (scale in ytickscales) {
|
||
min_scale = Math.min(min_scale, scale);
|
||
max_scale = Math.max(max_scale, scale);
|
||
}
|
||
root.data("min_scale", min_scale);
|
||
root.data("max_scale", max_scale);
|
||
|
||
// store the original positions of labels
|
||
if (xlabels) {
|
||
xlabels.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
element.data("x", element.asPX("x"));
|
||
});
|
||
}
|
||
|
||
if (ylabels) {
|
||
ylabels.selectAll("text")
|
||
.forEach(function (element, i) {
|
||
element.data("y", element.asPX("y"));
|
||
});
|
||
}
|
||
|
||
// mark grid lines and ticks as in or out of scale.
|
||
var mark_inscale = function (element, i) {
|
||
element.attribute("gadfly:inscale", element.attribute("gadfly:scale") == 1.0);
|
||
};
|
||
|
||
if (xgridlines) xgridlines.selectAll("path").forEach(mark_inscale);
|
||
if (ygridlines) ygridlines.selectAll("path").forEach(mark_inscale);
|
||
if (xlabels) xlabels.selectAll("text").forEach(mark_inscale);
|
||
if (ylabels) ylabels.selectAll("text").forEach(mark_inscale);
|
||
|
||
// figure out the upper ond lower bounds on panning using the maximum
|
||
// and minum grid lines
|
||
var bounds = root.plotbounds();
|
||
var pan_bounds = {
|
||
x0: 0.0,
|
||
y0: 0.0,
|
||
x1: 0.0,
|
||
y1: 0.0
|
||
};
|
||
|
||
if (xgridlines) {
|
||
xgridlines
|
||
.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var bbox = element.node.getBBox();
|
||
if (bounds.x1 - bbox.x < pan_bounds.x0) {
|
||
pan_bounds.x0 = bounds.x1 - bbox.x;
|
||
}
|
||
if (bounds.x0 - bbox.x > pan_bounds.x1) {
|
||
pan_bounds.x1 = bounds.x0 - bbox.x;
|
||
}
|
||
element.attr("visibility", "visible");
|
||
}
|
||
});
|
||
}
|
||
|
||
if (ygridlines) {
|
||
ygridlines
|
||
.selectAll("path")
|
||
.forEach(function (element, i) {
|
||
if (element.attribute("gadfly:inscale") == "true") {
|
||
var bbox = element.node.getBBox();
|
||
if (bounds.y1 - bbox.y < pan_bounds.y0) {
|
||
pan_bounds.y0 = bounds.y1 - bbox.y;
|
||
}
|
||
if (bounds.y0 - bbox.y > pan_bounds.y1) {
|
||
pan_bounds.y1 = bounds.y0 - bbox.y;
|
||
}
|
||
element.attr("visibility", "visible");
|
||
}
|
||
});
|
||
}
|
||
|
||
// nudge these values a little
|
||
pan_bounds.x0 -= 5;
|
||
pan_bounds.x1 += 5;
|
||
pan_bounds.y0 -= 5;
|
||
pan_bounds.y1 += 5;
|
||
root.data("pan_bounds", pan_bounds);
|
||
|
||
root.data("zoompan-ready", true)
|
||
};
|
||
|
||
|
||
// drag actions, i.e. zooming and panning
|
||
var pan_action = {
|
||
start: function(root, x, y, event) {
|
||
root.data("dx", 0);
|
||
root.data("dy", 0);
|
||
root.data("tx0", root.data("tx"));
|
||
root.data("ty0", root.data("ty"));
|
||
},
|
||
update: function(root, dx, dy, x, y, event) {
|
||
var px_per_mm = root.data("px_per_mm");
|
||
dx /= px_per_mm;
|
||
dy /= px_per_mm;
|
||
|
||
var tx0 = root.data("tx"),
|
||
ty0 = root.data("ty");
|
||
|
||
var dx0 = root.data("dx"),
|
||
dy0 = root.data("dy");
|
||
|
||
root.data("dx", dx);
|
||
root.data("dy", dy);
|
||
|
||
dx = dx - dx0;
|
||
dy = dy - dy0;
|
||
|
||
var tx = tx0 + dx,
|
||
ty = ty0 + dy;
|
||
|
||
set_plot_pan_zoom(root, tx, ty, root.data("scale"));
|
||
},
|
||
end: function(root, event) {
|
||
|
||
},
|
||
cancel: function(root) {
|
||
set_plot_pan_zoom(root, root.data("tx0"), root.data("ty0"), root.data("scale"));
|
||
}
|
||
};
|
||
|
||
var zoom_box;
|
||
var zoom_action = {
|
||
start: function(root, x, y, event) {
|
||
var bounds = root.plotbounds();
|
||
var width = bounds.x1 - bounds.x0,
|
||
height = bounds.y1 - bounds.y0;
|
||
var ratio = width / height;
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var px_per_mm = root.data("px_per_mm");
|
||
x = xscalable ? x / px_per_mm : bounds.x0;
|
||
y = yscalable ? y / px_per_mm : bounds.y0;
|
||
var w = xscalable ? 0 : width;
|
||
var h = yscalable ? 0 : height;
|
||
zoom_box = root.rect(x, y, w, h).attr({
|
||
"fill": "#000",
|
||
"opacity": 0.25
|
||
});
|
||
zoom_box.data("ratio", ratio);
|
||
},
|
||
update: function(root, dx, dy, x, y, event) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var px_per_mm = root.data("px_per_mm");
|
||
var bounds = root.plotbounds();
|
||
if (yscalable) {
|
||
y /= px_per_mm;
|
||
y = Math.max(bounds.y0, y);
|
||
y = Math.min(bounds.y1, y);
|
||
} else {
|
||
y = bounds.y1;
|
||
}
|
||
if (xscalable) {
|
||
x /= px_per_mm;
|
||
x = Math.max(bounds.x0, x);
|
||
x = Math.min(bounds.x1, x);
|
||
} else {
|
||
x = bounds.x1;
|
||
}
|
||
|
||
dx = x - zoom_box.attr("x");
|
||
dy = y - zoom_box.attr("y");
|
||
if (xscalable && yscalable) {
|
||
var ratio = zoom_box.data("ratio");
|
||
var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));
|
||
var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);
|
||
dx = width * dx / Math.abs(dx);
|
||
dy = height * dy / Math.abs(dy);
|
||
}
|
||
var xoffset = 0,
|
||
yoffset = 0;
|
||
if (dx < 0) {
|
||
xoffset = dx;
|
||
dx = -1 * dx;
|
||
}
|
||
if (dy < 0) {
|
||
yoffset = dy;
|
||
dy = -1 * dy;
|
||
}
|
||
if (isNaN(dy)) {
|
||
dy = 0.0;
|
||
}
|
||
if (isNaN(dx)) {
|
||
dx = 0.0;
|
||
}
|
||
zoom_box.transform("T" + xoffset + "," + yoffset);
|
||
zoom_box.attr("width", dx);
|
||
zoom_box.attr("height", dy);
|
||
},
|
||
end: function(root, event) {
|
||
var xscalable = root.hasClass("xscalable"),
|
||
yscalable = root.hasClass("yscalable");
|
||
var zoom_bounds = zoom_box.getBBox();
|
||
if (zoom_bounds.width * zoom_bounds.height <= 0) {
|
||
return;
|
||
}
|
||
var plot_bounds = root.plotbounds();
|
||
var zoom_factor = 1.0;
|
||
if (yscalable) {
|
||
zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;
|
||
} else {
|
||
zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;
|
||
}
|
||
var tx = (root.data("tx") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,
|
||
ty = (root.data("ty") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;
|
||
set_plot_pan_zoom(root, tx, ty, root.data("scale") * zoom_factor);
|
||
zoom_box.remove();
|
||
},
|
||
cancel: function(root) {
|
||
zoom_box.remove();
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onstart = function(x, y, event) {
|
||
var root = this.plotroot();
|
||
var scalable = root.hasClass("xscalable") || root.hasClass("yscalable");
|
||
var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;
|
||
var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;
|
||
var drag_action = zoomable ? zoom_action :
|
||
panable ? pan_action :
|
||
undefined;
|
||
root.data("drag_action", drag_action);
|
||
if (drag_action) {
|
||
var cancel_drag_action = function(event) {
|
||
if (event.which == 27) { // esc key
|
||
drag_action.cancel(root);
|
||
root.data("drag_action", undefined);
|
||
}
|
||
};
|
||
window.addEventListener("keyup", cancel_drag_action);
|
||
root.data("cancel_drag_action", cancel_drag_action);
|
||
drag_action.start(root, x, y, event);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {
|
||
var root = this.plotroot();
|
||
var drag_action = root.data("drag_action");
|
||
if (drag_action) {
|
||
drag_action.update(root, dx, dy, x, y, event);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_drag_onend = function(event) {
|
||
var root = this.plotroot();
|
||
window.removeEventListener("keyup", root.data("cancel_drag_action"));
|
||
root.data("cancel_drag_action", undefined);
|
||
var drag_action = root.data("drag_action");
|
||
if (drag_action) {
|
||
drag_action.end(root, event);
|
||
}
|
||
root.data("drag_action", undefined);
|
||
};
|
||
|
||
|
||
Gadfly.guide_background_scroll = function(event) {
|
||
if (event.shiftKey) {
|
||
increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);
|
||
event.preventDefault();
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_button_mouseover = function(event) {
|
||
this.select(".button_logo")
|
||
.animate({fill: this.data("mouseover_color")}, 100);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_button_mouseout = function(event) {
|
||
this.select(".button_logo")
|
||
.animate({fill: this.data("mouseout_color")}, 100);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_zoomout_click = function(event) {
|
||
increase_zoom_by_position(this.plotroot(), -0.1, true);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_zoomin_click = function(event) {
|
||
increase_zoom_by_position(this.plotroot(), 0.1, true);
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_track_click = function(event) {
|
||
// TODO
|
||
};
|
||
|
||
|
||
// Map slider position x to scale y using the function y = a*exp(b*x)+c.
|
||
// The constants a, b, and c are solved using the constraint that the function
|
||
// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).
|
||
var scale_from_slider_position = function(position, min_scale, max_scale) {
|
||
var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),
|
||
b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),
|
||
c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);
|
||
return a * Math.exp(b * position) + c;
|
||
}
|
||
|
||
// inverse of scale_from_slider_position
|
||
var slider_position_from_scale = function(scale, min_scale, max_scale) {
|
||
var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),
|
||
b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),
|
||
c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);
|
||
return 1 / b * Math.log((scale - c) / a);
|
||
}
|
||
|
||
var increase_zoom_by_position = function(root, delta_position, animate) {
|
||
var scale = root.data("scale"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale");
|
||
var position = slider_position_from_scale(scale, min_scale, max_scale);
|
||
position += delta_position;
|
||
scale = scale_from_slider_position(position, min_scale, max_scale);
|
||
set_zoom(root, scale, animate);
|
||
}
|
||
|
||
var set_zoom = function(root, scale, animate) {
|
||
var min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale"),
|
||
old_scale = root.data("scale");
|
||
var new_scale = Math.max(min_scale, Math.min(scale, max_scale));
|
||
if (animate) {
|
||
Snap.animate(
|
||
old_scale,
|
||
new_scale,
|
||
function (new_scale) {
|
||
update_plot_scale(root, new_scale);
|
||
},
|
||
200);
|
||
} else {
|
||
update_plot_scale(root, new_scale);
|
||
}
|
||
}
|
||
|
||
|
||
var update_plot_scale = function(root, new_scale) {
|
||
var trans = scale_centered_translation(root, new_scale);
|
||
set_plot_pan_zoom(root, trans.x, trans.y, new_scale);
|
||
|
||
root.selectAll(".zoomslider_thumb")
|
||
.forEach(function (element, i) {
|
||
var min_pos = element.data("min_pos"),
|
||
max_pos = element.data("max_pos"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale");
|
||
var xmid = (min_pos + max_pos) / 2;
|
||
var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);
|
||
element.transform(new Snap.Matrix().translate(
|
||
Math.max(min_pos, Math.min(
|
||
max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));
|
||
});
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {
|
||
var root = this.plotroot();
|
||
var min_pos = this.data("min_pos"),
|
||
max_pos = this.data("max_pos"),
|
||
min_scale = root.data("min_scale"),
|
||
max_scale = root.data("max_scale"),
|
||
old_scale = root.data("old_scale");
|
||
|
||
var px_per_mm = root.data("px_per_mm");
|
||
dx /= px_per_mm;
|
||
dy /= px_per_mm;
|
||
|
||
var xmid = (min_pos + max_pos) / 2;
|
||
var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +
|
||
dx / (max_pos - min_pos);
|
||
|
||
// compute the new scale
|
||
var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);
|
||
new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));
|
||
|
||
update_plot_scale(root, new_scale);
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {
|
||
this.animate({fill: this.data("mouseover_color")}, 100);
|
||
var root = this.plotroot();
|
||
|
||
// keep track of what the scale was when we started dragging
|
||
root.data("old_scale", root.data("scale"));
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
Gadfly.zoomslider_thumb_dragend = function(event) {
|
||
this.animate({fill: this.data("mouseout_color")}, 100);
|
||
event.stopPropagation();
|
||
};
|
||
|
||
|
||
var toggle_color_class = function(root, color_class, ison) {
|
||
var guides = root.selectAll(".guide." + color_class + ",.guide ." + color_class);
|
||
var geoms = root.selectAll(".geometry." + color_class + ",.geometry ." + color_class);
|
||
if (ison) {
|
||
guides.animate({opacity: 0.5}, 250);
|
||
geoms.animate({opacity: 0.0}, 250);
|
||
} else {
|
||
guides.animate({opacity: 1.0}, 250);
|
||
geoms.animate({opacity: 1.0}, 250);
|
||
}
|
||
};
|
||
|
||
|
||
Gadfly.colorkey_swatch_click = function(event) {
|
||
var root = this.plotroot();
|
||
var color_class = this.data("color_class");
|
||
|
||
if (event.shiftKey) {
|
||
root.selectAll(".colorkey text")
|
||
.forEach(function (element) {
|
||
var other_color_class = element.data("color_class");
|
||
if (other_color_class != color_class) {
|
||
toggle_color_class(root, other_color_class,
|
||
element.attr("opacity") == 1.0);
|
||
}
|
||
});
|
||
} else {
|
||
toggle_color_class(root, color_class, this.attr("opacity") == 1.0);
|
||
}
|
||
};
|
||
|
||
|
||
return Gadfly;
|
||
|
||
}));
|
||
|
||
|
||
//@ sourceURL=gadfly.js
|
||
|
||
(function (glob, factory) {
|
||
// AMD support
|
||
if (typeof require === "function" && typeof define === "function" && define.amd) {
|
||
require(["Snap.svg", "Gadfly"], function (Snap, Gadfly) {
|
||
factory(Snap, Gadfly);
|
||
});
|
||
} else {
|
||
factory(glob.Snap, glob.Gadfly);
|
||
}
|
||
})(window, function (Snap, Gadfly) {
|
||
var fig = Snap("#fig-0378e04b897742b597befd2e8e1c169e");
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-8")
|
||
.init_gadfly();
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-11")
|
||
.plotroot().data("unfocused_ygrid_color", "#D0D0E0")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-11")
|
||
.plotroot().data("focused_ygrid_color", "#A0A0A0")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-12")
|
||
.plotroot().data("unfocused_xgrid_color", "#D0D0E0")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-12")
|
||
.plotroot().data("focused_xgrid_color", "#A0A0A0")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-16")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-16")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-16")
|
||
.click(Gadfly.zoomslider_zoomin_click)
|
||
.mouseenter(Gadfly.zoomslider_button_mouseover)
|
||
.mouseleave(Gadfly.zoomslider_button_mouseout)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-18")
|
||
.data("max_pos", 111.58)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-18")
|
||
.data("min_pos", 94.58)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-18")
|
||
.click(Gadfly.zoomslider_track_click);
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-19")
|
||
.data("max_pos", 111.58)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-19")
|
||
.data("min_pos", 94.58)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-19")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-19")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-19")
|
||
.drag(Gadfly.zoomslider_thumb_dragmove,
|
||
Gadfly.zoomslider_thumb_dragstart,
|
||
Gadfly.zoomslider_thumb_dragend)
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
|
||
.data("mouseover_color", "#CD5C5C")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
|
||
.data("mouseout_color", "#6A6A6A")
|
||
;
|
||
fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
|
||
.click(Gadfly.zoomslider_zoomout_click)
|
||
.mouseenter(Gadfly.zoomslider_button_mouseover)
|
||
.mouseleave(Gadfly.zoomslider_button_mouseout)
|
||
;
|
||
});
|
||
]]> </script>
|
||
</svg>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="cell border-box-sizing text_cell rendered">
|
||
<div class="prompt input_prompt">
|
||
</div>
|
||
<div class="inner_cell">
|
||
<div class="text_cell_render border-box-sizing rendered_html">
|
||
<h3 id="Final-simulation">Final simulation<a class="anchor-link" href="#Final-simulation">¶</a></h3><p>We're now going to actually build out the full motion that we'll use for computing the pricing of our autocallable products. It will be largely the same, but we will use far more sample paths for the simulation.</p>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [8]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">full_simulation</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">term</span><span class="p">)</span>
|
||
<span class="n">forward</span> <span class="o">=</span> <span class="n">vcat</span><span class="p">(</span><span class="n">term</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">forward_term</span><span class="p">(</span><span class="n">term</span><span class="p">))</span>
|
||
|
||
<span class="c"># And an S0 to kick things off.</span>
|
||
<span class="n">final_motion</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">*</span> <span class="n">S0</span>
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">T</span>
|
||
<span class="n">μ</span> <span class="o">=</span> <span class="p">(</span><span class="n">forward</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">q</span><span class="p">)</span>
|
||
<span class="n">year_motion</span> <span class="o">=</span> <span class="n">simulate_gbm</span><span class="p">(</span><span class="n">final_motion</span><span class="p">[:,</span><span class="k">end</span><span class="p">],</span> <span class="n">μ</span><span class="p">,</span> <span class="n">σ</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
|
||
<span class="n">final_motion</span> <span class="o">=</span> <span class="n">hcat</span><span class="p">(</span><span class="n">final_motion</span><span class="p">,</span> <span class="n">year_motion</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">return</span> <span class="n">final_motion</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">tic</span><span class="p">()</span>
|
||
<span class="n">full_simulation</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">term</span><span class="p">)</span>
|
||
<span class="n">time</span> <span class="o">=</span> <span class="n">toq</span><span class="p">()</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Time to run simulation: %.2fs"</span><span class="p">,</span> <span class="n">time</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="output_wrapper">
|
||
<div class="output">
|
||
|
||
|
||
<div class="output_area"><div class="prompt"></div>
|
||
<div class="output_subarea output_stream output_stdout output_text">
|
||
<pre>Time to run simulation: 5.34s</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="cell border-box-sizing text_cell rendered">
|
||
<div class="prompt input_prompt">
|
||
</div>
|
||
<div class="inner_cell">
|
||
<div class="text_cell_render border-box-sizing rendered_html">
|
||
<h2 id="Athena-Simulation">Athena Simulation<a class="anchor-link" href="#Athena-Simulation">¶</a></h2><p>Now that we've defined our underlying simulation, let's actually try and price an Athena note. Athena has the following characteristics:</p>
|
||
<ul>
|
||
<li>Automatically called if the underlying is above the <strong>call barrier</strong> at observation</li>
|
||
<li>Accelerated coupon paid if the underlying is above the <strong>call barrier</strong> at observation<ul>
|
||
<li>The coupon paid is $c \cdot i$ with $i$ as the current year, and $c$ the coupon rate</li>
|
||
</ul>
|
||
</li>
|
||
<li>Principle protection up until a <strong>protection barrier</strong> at observation; All principle at risk if this barrier not met</li>
|
||
<li>Observed yearly</li>
|
||
</ul>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [9]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">call_barrier</span> <span class="o">=</span> <span class="n">S0</span>
|
||
<span class="n">strike</span> <span class="o">=</span> <span class="n">S0</span>
|
||
<span class="n">protection_barrier</span> <span class="o">=</span> <span class="n">S0</span> <span class="o">*</span> <span class="o">.</span><span class="mi">6</span>
|
||
<span class="n">coupon</span> <span class="o">=</span> <span class="n">nominal</span> <span class="o">*</span> <span class="o">.</span><span class="mi">07</span>
|
||
|
||
<span class="n">price_athena</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">initial_price</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span> <span class="n">call_barrier</span><span class="p">,</span>
|
||
<span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">total_coupons</span> <span class="o">=</span> <span class="mi">0</span>
|
||
|
||
<span class="n">t</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">year_prices</span><span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">t</span>
|
||
<span class="n">price</span> <span class="o">=</span> <span class="n">year_prices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||
<span class="k">if</span> <span class="n">price</span> <span class="n">≥</span> <span class="n">call_barrier</span>
|
||
<span class="k">return</span> <span class="p">(</span><span class="n">nominal</span> <span class="o">+</span> <span class="n">coupon</span><span class="o">*</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">exp</span><span class="p">((</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="k">end</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="o">-</span><span class="n">i</span><span class="p">))</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="c"># We've reached maturity, time to check capital protection</span>
|
||
<span class="k">if</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">]</span> <span class="o">></span> <span class="n">protection_barrier</span>
|
||
<span class="k">return</span> <span class="n">nominal</span>
|
||
<span class="k">else</span>
|
||
<span class="n">put</span> <span class="o">=</span> <span class="p">(</span><span class="n">strike</span> <span class="o">-</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">])</span> <span class="o">/</span> <span class="n">strike</span>
|
||
<span class="k">return</span> <span class="n">nominal</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">put</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">forward_structure</span> <span class="o">=</span> <span class="n">forward_term</span><span class="p">(</span><span class="n">term</span><span class="p">)</span>
|
||
<span class="n">price_function</span> <span class="o">=</span> <span class="p">(</span><span class="n">year_prices</span><span class="p">)</span> <span class="o">-></span> <span class="n">price_athena</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span>
|
||
<span class="n">call_barrier</span><span class="p">,</span> <span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">athena</span> <span class="o">=</span> <span class="n">function</span><span class="p">()</span>
|
||
<span class="n">year_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="o">*</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">T</span><span class="p">]</span>
|
||
<span class="n">motion</span> <span class="o">=</span> <span class="n">full_simulation</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">term</span><span class="p">)</span>
|
||
<span class="n">payoffs</span> <span class="o">=</span> <span class="p">[</span><span class="n">price_function</span><span class="p">(</span><span class="n">motion</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">year_indexes</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">m</span><span class="p">]</span>
|
||
<span class="k">return</span> <span class="n">mean</span><span class="p">(</span><span class="n">payoffs</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">mean_payoffs</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">num_simulations</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">num_simulations</span>
|
||
<span class="n">tic</span><span class="p">()</span>
|
||
<span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">athena</span><span class="p">()</span>
|
||
<span class="n">time</span> <span class="o">=</span> <span class="n">toq</span><span class="p">()</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Mean of simulation %i: \</span><span class="si">$</span><span class="s">%.4f; Simulation time: %.2fs</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">time</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">final_mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">mean_payoffs</span><span class="p">)</span>
|
||
<span class="n">println</span><span class="p">(</span><span class="s">"Mean over </span><span class="si">$</span><span class="s">num_simulations simulations: </span><span class="si">$</span><span class="s">(mean(mean_payoffs))"</span><span class="p">)</span>
|
||
<span class="n">pv</span> <span class="o">=</span> <span class="n">final_mean</span> <span class="o">*</span> <span class="p">(</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">T</span><span class="p">))</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Present value of Athena note: \</span><span class="si">$</span><span class="s">%.2f, notional: \</span><span class="si">$</span><span class="s">%.2f"</span><span class="p">,</span> <span class="n">pv</span><span class="p">,</span> <span class="n">nominal</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="output_wrapper">
|
||
<div class="output">
|
||
|
||
|
||
<div class="output_area"><div class="prompt"></div>
|
||
<div class="output_subarea output_stream output_stdout output_text">
|
||
<pre>Mean of simulation 1: $103.2805; Simulation time: 5.59s
|
||
Mean of simulation 2: $103.3796; Simulation time: 5.05s
|
||
Mean of simulation 3: $103.4752; Simulation time: 5.18s
|
||
Mean of simulation 4: $103.4099; Simulation time: 5.37s
|
||
Mean of simulation 5: $103.3260; Simulation time: 5.32s
|
||
Mean over 5 simulations: 103.37421610015554
|
||
Present value of Athena note: $95.00, notional: $100.00</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="cell border-box-sizing text_cell rendered">
|
||
<div class="prompt input_prompt">
|
||
</div>
|
||
<div class="inner_cell">
|
||
<div class="text_cell_render border-box-sizing rendered_html">
|
||
<h2 id="Phoenix-without-Memory-Simulation">Phoenix without Memory Simulation<a class="anchor-link" href="#Phoenix-without-Memory-Simulation">¶</a></h2><p>Let's move into pricing a Phoenix without memory. It's very similar to the Athena production, with the exception that we introduce a coupon barrier so coupons are paid even when the underlying is below the initial price.</p>
|
||
<p>The Phoenix product has the following characteristics (example <a href="https://www.rbccm.com/usstructurednotes/file-780079.pdf">here</a>):</p>
|
||
<ul>
|
||
<li>Automatically called if the underlying is above the <strong>call barrier</strong> at observation</li>
|
||
<li>Coupon paid if the underlying is above a <strong>coupon barrier</strong> at observation</li>
|
||
<li>Principle protection up until a <strong>protection barrier</strong> at observation; All principle at risk if this barrier not met</li>
|
||
<li>Observed yearly</li>
|
||
</ul>
|
||
<p>Some example paths (all assume that a call barrier of the current price, and coupon barrier some level below that):</p>
|
||
<ul>
|
||
<li>At the end of year 1, the stock is above the call barrier; the note is called and you receive the value of the stock plus the coupon being paid.</li>
|
||
<li>At the end of year 1, the stock is above the coupon barrier, but not the call barrier; you receive the coupon. At the end of year 2, the stock is below the coupon barrier; you receive nothing. At the end of year 3, the stock is above the call barrier; the note is called and you receive the value of the stock plus a coupon for year 3.</li>
|
||
</ul>
|
||
<p>We're going to re-use the same simulation, with the following parameters:</p>
|
||
<ul>
|
||
<li>Call barrier: 100%</li>
|
||
<li>Coupon barrier: 70%</li>
|
||
<li>Coupon: 6%</li>
|
||
<li>Capital protection until 70% (at maturity)</li>
|
||
</ul>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [10]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">call_barrier</span> <span class="o">=</span> <span class="n">S0</span>
|
||
<span class="n">coupon_barrier</span> <span class="o">=</span> <span class="n">S0</span> <span class="o">*</span> <span class="o">.</span><span class="mi">8</span>
|
||
<span class="n">protection_barrier</span> <span class="o">=</span> <span class="n">S0</span> <span class="o">*</span> <span class="o">.</span><span class="mi">6</span>
|
||
<span class="n">coupon</span> <span class="o">=</span> <span class="n">nominal</span> <span class="o">*</span> <span class="o">.</span><span class="mi">06</span>
|
||
|
||
<span class="n">price_phoenix_no_memory</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">initial_price</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span> <span class="n">call_barrier</span><span class="p">,</span> <span class="n">coupon_barrier</span><span class="p">,</span>
|
||
<span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">total_coupons</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<span class="n">t</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">year_prices</span><span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">t</span>
|
||
<span class="n">price</span> <span class="o">=</span> <span class="n">year_prices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||
<span class="k">if</span> <span class="n">price</span> <span class="n">≥</span> <span class="n">call_barrier</span>
|
||
<span class="k">return</span> <span class="p">(</span><span class="n">nominal</span> <span class="o">+</span> <span class="n">coupon</span> <span class="o">+</span> <span class="n">total_coupons</span><span class="p">)</span><span class="o">*</span><span class="n">exp</span><span class="p">((</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="k">end</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="o">-</span><span class="n">i</span><span class="p">))</span>
|
||
<span class="k">elseif</span> <span class="n">price</span> <span class="n">≥</span> <span class="n">coupon_barrier</span>
|
||
<span class="n">total_coupons</span> <span class="o">=</span> <span class="n">total_coupons</span> <span class="o">*</span> <span class="n">exp</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">coupon</span>
|
||
<span class="k">else</span>
|
||
<span class="n">total_coupons</span> <span class="o">*=</span> <span class="n">exp</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="c"># We've reached maturity, time to check capital protection</span>
|
||
<span class="k">if</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">]</span> <span class="o">></span> <span class="n">protection_barrier</span>
|
||
<span class="k">return</span> <span class="n">nominal</span> <span class="o">+</span> <span class="n">total_coupons</span>
|
||
<span class="k">else</span>
|
||
<span class="n">put</span> <span class="o">=</span> <span class="p">(</span><span class="n">strike</span> <span class="o">-</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">])</span> <span class="o">/</span> <span class="n">strike</span>
|
||
<span class="k">return</span> <span class="n">nominal</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">put</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">forward_structure</span> <span class="o">=</span> <span class="n">forward_term</span><span class="p">(</span><span class="n">term</span><span class="p">)</span>
|
||
<span class="n">price_function</span> <span class="o">=</span> <span class="p">(</span><span class="n">year_prices</span><span class="p">)</span> <span class="o">-></span> <span class="n">price_phoenix_no_memory</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span>
|
||
<span class="n">call_barrier</span><span class="p">,</span> <span class="n">coupon_barrier</span><span class="p">,</span> <span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">phoenix_no_memory</span> <span class="o">=</span> <span class="n">function</span><span class="p">()</span>
|
||
<span class="n">year_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="o">*</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">T</span><span class="p">]</span>
|
||
<span class="n">motion</span> <span class="o">=</span> <span class="n">full_simulation</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">term</span><span class="p">)</span>
|
||
<span class="n">payoffs</span> <span class="o">=</span> <span class="p">[</span><span class="n">price_function</span><span class="p">(</span><span class="n">motion</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">year_indexes</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">m</span><span class="p">]</span>
|
||
<span class="k">return</span> <span class="n">mean</span><span class="p">(</span><span class="n">payoffs</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">mean_payoffs</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">num_simulations</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">num_simulations</span>
|
||
<span class="n">tic</span><span class="p">()</span>
|
||
<span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">phoenix_no_memory</span><span class="p">()</span>
|
||
<span class="n">time</span> <span class="o">=</span> <span class="n">toq</span><span class="p">()</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Mean of simulation %i: \</span><span class="si">$</span><span class="s">%.4f; Simulation time: %.2fs</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">time</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">final_mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">mean_payoffs</span><span class="p">)</span>
|
||
<span class="n">println</span><span class="p">(</span><span class="s">"Mean over </span><span class="si">$</span><span class="s">num_simulations simulations: </span><span class="si">$</span><span class="s">(mean(mean_payoffs))"</span><span class="p">)</span>
|
||
<span class="n">pv</span> <span class="o">=</span> <span class="n">final_mean</span> <span class="o">*</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">T</span><span class="p">))</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Present value of Phoenix without memory note: \</span><span class="si">$</span><span class="s">%.2f"</span><span class="p">,</span> <span class="n">pv</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="output_wrapper">
|
||
<div class="output">
|
||
|
||
|
||
<div class="output_area"><div class="prompt"></div>
|
||
<div class="output_subarea output_stream output_stdout output_text">
|
||
<pre>Mean of simulation 1: $106.0562; Simulation time: 5.72s
|
||
Mean of simulation 2: $106.0071; Simulation time: 5.85s
|
||
Mean of simulation 3: $105.9959; Simulation time: 5.87s
|
||
Mean of simulation 4: $106.0665; Simulation time: 5.93s
|
||
Mean of simulation 5: $106.0168; Simulation time: 5.81s
|
||
Mean over 5 simulations: 106.02850857209883
|
||
Present value of Phoenix without memory note: $97.44</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="cell border-box-sizing text_cell rendered">
|
||
<div class="prompt input_prompt">
|
||
</div>
|
||
<div class="inner_cell">
|
||
<div class="text_cell_render border-box-sizing rendered_html">
|
||
<h2 id="Phoenix-with-Memory-Simulation">Phoenix with Memory Simulation<a class="anchor-link" href="#Phoenix-with-Memory-Simulation">¶</a></h2><p>The Phoenix with Memory structure is very similar to the Phoenix, but as the name implies, has a special "memory" property: <strong>It remembers any coupons that haven't been paid at prior observation times, and pays them all if the underlying crosses the coupon barrier</strong>. For example:</p>
|
||
<ul>
|
||
<li>Note issued with 100% call barrier, 70% coupon barrier. At year 1, the underlying is at 50%, so no coupons are paid. At year 2, the underlying is at 80%, so coupons for both year 1 and 2 are paid, resulting in a double coupon.</li>
|
||
</ul>
|
||
<p>You can also find an example <a href="https://www.rbccm.com/usstructurednotes/file-781232.pdf">here</a>.</p>
|
||
<p>Let's go ahead and set up the simulation! The parameters will be the same, but we can expect that the value will go up because of the memory attribute</p>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="cell border-box-sizing code_cell rendered">
|
||
<div class="input">
|
||
<div class="prompt input_prompt">In [11]:</div>
|
||
<div class="inner_cell">
|
||
<div class="input_area">
|
||
<div class=" highlight hl-julia"><pre><span class="n">call_barrier</span> <span class="o">=</span> <span class="n">S0</span>
|
||
<span class="n">coupon_barrier</span> <span class="o">=</span> <span class="n">S0</span> <span class="o">*</span> <span class="o">.</span><span class="mi">8</span>
|
||
<span class="n">protection_barrier</span> <span class="o">=</span> <span class="n">S0</span> <span class="o">*</span> <span class="o">.</span><span class="mi">6</span>
|
||
<span class="n">coupon</span> <span class="o">=</span> <span class="n">nominal</span> <span class="o">*</span> <span class="o">.</span><span class="mi">07</span>
|
||
|
||
<span class="n">price_phoenix_with_memory</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">initial_price</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span> <span class="n">call_barrier</span><span class="p">,</span>
|
||
<span class="n">coupon_barrier</span><span class="p">,</span> <span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">last_coupon</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<span class="n">total_coupons</span> <span class="o">=</span> <span class="mi">0</span>
|
||
|
||
<span class="n">t</span> <span class="o">=</span> <span class="n">length</span><span class="p">(</span><span class="n">year_prices</span><span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">t</span>
|
||
<span class="n">price</span> <span class="o">=</span> <span class="n">year_prices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||
<span class="k">if</span> <span class="n">price</span> <span class="o">></span> <span class="n">call_barrier</span>
|
||
<span class="k">return</span> <span class="p">(</span><span class="n">nominal</span> <span class="o">+</span> <span class="n">coupon</span> <span class="o">+</span> <span class="n">total_coupons</span><span class="p">)</span><span class="o">*</span><span class="n">exp</span><span class="p">((</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="k">end</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="o">-</span><span class="n">i</span><span class="p">))</span>
|
||
<span class="k">elseif</span> <span class="n">price</span> <span class="o">></span> <span class="n">coupon_barrier</span>
|
||
<span class="c">####################################################################</span>
|
||
<span class="c"># The only difference between with/without memory is the below lines</span>
|
||
<span class="n">memory_coupons</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="n">last_coupon</span><span class="p">)</span> <span class="o">*</span> <span class="n">coupon</span>
|
||
<span class="n">last_coupon</span> <span class="o">=</span> <span class="n">i</span>
|
||
<span class="n">total_coupons</span> <span class="o">=</span> <span class="n">total_coupons</span> <span class="o">*</span> <span class="n">exp</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">memory_coupons</span>
|
||
<span class="c">####################################################################</span>
|
||
<span class="k">else</span>
|
||
<span class="n">total_coupons</span> <span class="o">*=</span> <span class="n">exp</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="c"># We've reached maturity, time to check capital protection</span>
|
||
<span class="k">if</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">]</span> <span class="o">></span> <span class="n">protection_barrier</span>
|
||
<span class="k">return</span> <span class="n">nominal</span> <span class="o">+</span> <span class="n">total_coupons</span>
|
||
<span class="k">else</span>
|
||
<span class="n">put</span> <span class="o">=</span> <span class="p">(</span><span class="n">strike</span> <span class="o">-</span> <span class="n">year_prices</span><span class="p">[</span><span class="k">end</span><span class="p">])</span> <span class="o">/</span> <span class="n">strike</span>
|
||
<span class="k">return</span> <span class="n">nominal</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">put</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">forward_structure</span> <span class="o">=</span> <span class="n">forward_term</span><span class="p">(</span><span class="n">term</span><span class="p">)</span>
|
||
<span class="n">price_function</span> <span class="o">=</span> <span class="p">(</span><span class="n">year_prices</span><span class="p">)</span> <span class="o">-></span> <span class="n">price_phoenix_with_memory</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">year_prices</span><span class="p">,</span>
|
||
<span class="n">call_barrier</span><span class="p">,</span> <span class="n">coupon_barrier</span><span class="p">,</span> <span class="n">protection_barrier</span><span class="p">,</span> <span class="n">coupon</span><span class="p">,</span> <span class="n">forward_structure</span><span class="p">)</span>
|
||
|
||
<span class="n">phoenix_with_memory</span> <span class="o">=</span> <span class="n">function</span><span class="p">()</span>
|
||
<span class="n">year_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="o">*</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">T</span><span class="p">]</span>
|
||
<span class="n">motion</span> <span class="o">=</span> <span class="n">full_simulation</span><span class="p">(</span><span class="n">S0</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">term</span><span class="p">)</span>
|
||
<span class="n">payoffs</span> <span class="o">=</span> <span class="p">[</span><span class="n">price_function</span><span class="p">(</span><span class="n">motion</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">year_indexes</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">m</span><span class="p">]</span>
|
||
<span class="k">return</span> <span class="n">mean</span><span class="p">(</span><span class="n">payoffs</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">mean_payoffs</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">num_simulations</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">i</span><span class="o">=</span><span class="mi">1</span><span class="p">:</span><span class="n">num_simulations</span>
|
||
<span class="n">tic</span><span class="p">()</span>
|
||
<span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">phoenix_with_memory</span><span class="p">()</span>
|
||
<span class="n">time</span> <span class="o">=</span> <span class="n">toq</span><span class="p">()</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Mean of simulation %i: \</span><span class="si">$</span><span class="s">%.4f; Simulation time: %.2fs</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span>
|
||
<span class="n">i</span><span class="p">,</span> <span class="n">mean_payoffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">time</span><span class="p">)</span>
|
||
<span class="k">end</span>
|
||
|
||
<span class="n">final_mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">mean_payoffs</span><span class="p">)</span>
|
||
<span class="n">println</span><span class="p">(</span><span class="s">"Mean over </span><span class="si">$</span><span class="s">num_simulations simulations: </span><span class="si">$</span><span class="s">(mean(mean_payoffs))"</span><span class="p">)</span>
|
||
<span class="n">pv</span> <span class="o">=</span> <span class="n">final_mean</span> <span class="o">*</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">forward_structure</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">T</span><span class="p">))</span>
|
||
<span class="p">@</span><span class="n">printf</span><span class="p">(</span><span class="s">"Present value of Phoenix with memory note: \</span><span class="si">$</span><span class="s">%.2f"</span><span class="p">,</span> <span class="n">pv</span><span class="p">)</span>
|
||
</pre></div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="output_wrapper">
|
||
<div class="output">
|
||
|
||
|
||
<div class="output_area"><div class="prompt"></div>
|
||
<div class="output_subarea output_stream output_stdout output_text">
|
||
<pre>Mean of simulation 1: $108.8612; Simulation time: 5.89s
|
||
Mean of simulation 2: $109.0226; Simulation time: 5.90s
|
||
Mean of simulation 3: $108.9175; Simulation time: 5.92s
|
||
Mean of simulation 4: $108.9426; Simulation time: 5.94s
|
||
Mean of simulation 5: $108.8087; Simulation time: 6.06s
|
||
Mean over 5 simulations: 108.91052564051816
|
||
Present value of Phoenix with memory note: $100.09</pre>
|
||
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|
||
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