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Add a new Rust article!
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Bradlee Speice
@ -4,7 +4,7 @@
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="description" content="Hello! Glad to meet you. I'm currently a student at Columbia University studying Financial Engineering, and want to give an overview of the projects I'm working on! To start things off, Columbia has ...">
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<meta name="description" content="Hello! Glad to meet you. I'm currently a student at Columbia University studying Financial Engineering, and want to give an overview of the projects I'm working on! To start things off, Columbia ...">
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<meta name="keywords" content="introduction, trading">
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<link rel="icon" href="https://bspeice.github.io/favicon.ico">
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@ -161,12 +161,12 @@ to make some tweaks over the coming weeks, and do another forward test in Januar
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<span class="kn">import</span> <span class="nn">Quandl</span>
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<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
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<span class="n">tickers</span> <span class="o">=</span> <span class="p">[</span><span class="s">'XOM'</span><span class="p">,</span> <span class="s">'CVX'</span><span class="p">,</span> <span class="s">'CLB'</span><span class="p">,</span> <span class="s">'OXY'</span><span class="p">,</span> <span class="s">'SLB'</span><span class="p">]</span>
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<span class="n">market_ticker</span> <span class="o">=</span> <span class="s">'GOOG/NYSE_VOO'</span>
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<span class="n">tickers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'XOM'</span><span class="p">,</span> <span class="s1">'CVX'</span><span class="p">,</span> <span class="s1">'CLB'</span><span class="p">,</span> <span class="s1">'OXY'</span><span class="p">,</span> <span class="s1">'SLB'</span><span class="p">]</span>
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<span class="n">market_ticker</span> <span class="o">=</span> <span class="s1">'GOOG/NYSE_VOO'</span>
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<span class="n">lookback</span> <span class="o">=</span> <span class="mi">30</span>
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<span class="n">d_col</span> <span class="o">=</span> <span class="s">'Close'</span>
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<span class="n">d_col</span> <span class="o">=</span> <span class="s1">'Close'</span>
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<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="n">tick</span><span class="p">:</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">'YAHOO/{}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tick</span><span class="p">))[</span><span class="o">-</span><span class="n">lookback</span><span class="p">:]</span> <span class="k">for</span> <span class="n">tick</span> <span class="ow">in</span> <span class="n">tickers</span><span class="p">}</span>
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<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="n">tick</span><span class="p">:</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'YAHOO/{}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tick</span><span class="p">))[</span><span class="o">-</span><span class="n">lookback</span><span class="p">:]</span> <span class="k">for</span> <span class="n">tick</span> <span class="ow">in</span> <span class="n">tickers</span><span class="p">}</span>
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<span class="n">market</span> <span class="o">=</span> <span class="n">Quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">market_ticker</span><span class="p">)</span>
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</pre></div>
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@ -340,22 +340,22 @@ s.t.\ \ & \vec{1} \omega = 1\\
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<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="k">import</span> <span class="n">minimize</span>
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<span class="c">#sharpe_limit = .1</span>
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<span class="c1">#sharpe_limit = .1</span>
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<span class="n">drawdown_limit</span> <span class="o">=</span> <span class="o">.</span><span class="mi">05</span>
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<span class="n">leverage</span> <span class="o">=</span> <span class="mi">250</span>
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<span class="c"># Use the map so we can guarantee we maintain the correct order</span>
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<span class="c"># sharpe_a = np.array(list(map(lambda tick: sharpes[tick], tickers))) * -1 # So we can write as upper-bound</span>
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<span class="c1"># Use the map so we can guarantee we maintain the correct order</span>
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<span class="c1"># sharpe_a = np.array(list(map(lambda tick: sharpes[tick], tickers))) * -1 # So we can write as upper-bound</span>
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<span class="n">dd_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">tick</span><span class="p">:</span> <span class="n">drawdowns</span><span class="p">[</span><span class="n">tick</span><span class="p">],</span> <span class="n">tickers</span><span class="p">)))</span>
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<span class="n">returns_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">tick</span><span class="p">:</span> <span class="n">returns</span><span class="p">[</span><span class="n">tick</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">tickers</span><span class="p">)))</span> <span class="c"># Because minimizing</span>
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<span class="n">returns_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">tick</span><span class="p">:</span> <span class="n">returns</span><span class="p">[</span><span class="n">tick</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">tickers</span><span class="p">)))</span> <span class="c1"># Because minimizing</span>
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<span class="n">meets_sharpe</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">sharpe_a</span><span class="p">)</span> <span class="o">-</span> <span class="n">sharpe_limit</span>
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<span class="k">def</span> <span class="nf">meets_dd</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
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<span class="n">portfolio</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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<span class="k">if</span> <span class="n">portfolio</span> <span class="o"><</span> <span class="o">.</span><span class="mi">1</span><span class="p">:</span>
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<span class="c"># If there are no stocks in the portfolio,</span>
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<span class="c"># we can accidentally induce division by 0,</span>
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<span class="c"># or division by something small enough to cause infinity</span>
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<span class="c1"># If there are no stocks in the portfolio,</span>
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<span class="c1"># we can accidentally induce division by 0,</span>
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<span class="c1"># or division by something small enough to cause infinity</span>
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<span class="k">return</span> <span class="mi">0</span>
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<span class="k">return</span> <span class="n">drawdown_limit</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">dd_a</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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@ -365,41 +365,41 @@ s.t.\ \ & \vec{1} \omega = 1\\
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<span class="k">def</span> <span class="nf">within_leverage</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
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<span class="k">return</span> <span class="n">leverage</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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<span class="n">objective</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">returns_a</span><span class="p">)</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="c"># Because we're minimizing</span>
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<span class="n">bounds</span> <span class="o">=</span> <span class="p">((</span><span class="k">None</span><span class="p">,</span> <span class="k">None</span><span class="p">),)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">tickers</span><span class="p">)</span>
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<span class="n">objective</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">returns_a</span><span class="p">)</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># Because we're minimizing</span>
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<span class="n">bounds</span> <span class="o">=</span> <span class="p">((</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">tickers</span><span class="p">)</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tickers</span><span class="p">))</span>
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<span class="n">constraints</span> <span class="o">=</span> <span class="p">[</span>
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<span class="p">{</span>
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<span class="s">'type'</span><span class="p">:</span> <span class="s">'eq'</span><span class="p">,</span>
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<span class="s">'fun'</span><span class="p">:</span> <span class="n">is_portfolio</span>
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<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'eq'</span><span class="p">,</span>
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<span class="s1">'fun'</span><span class="p">:</span> <span class="n">is_portfolio</span>
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<span class="p">},</span> <span class="p">{</span>
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<span class="s">'type'</span><span class="p">:</span> <span class="s">'ineq'</span><span class="p">,</span>
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<span class="s">'fun'</span><span class="p">:</span> <span class="n">within_leverage</span>
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<span class="c">#}, {</span>
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<span class="c"># 'type': 'ineq',</span>
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<span class="c"># 'fun': meets_sharpe</span>
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<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'ineq'</span><span class="p">,</span>
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<span class="s1">'fun'</span><span class="p">:</span> <span class="n">within_leverage</span>
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<span class="c1">#}, {</span>
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<span class="c1"># 'type': 'ineq',</span>
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<span class="c1"># 'fun': meets_sharpe</span>
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<span class="p">},</span> <span class="p">{</span>
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<span class="s">'type'</span><span class="p">:</span> <span class="s">'ineq'</span><span class="p">,</span>
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<span class="s">'fun'</span><span class="p">:</span> <span class="n">meets_dd</span>
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<span class="s1">'type'</span><span class="p">:</span> <span class="s1">'ineq'</span><span class="p">,</span>
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<span class="s1">'fun'</span><span class="p">:</span> <span class="n">meets_dd</span>
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<span class="p">}</span>
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<span class="p">]</span>
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<span class="n">optimal</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="n">bounds</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraints</span><span class="p">,</span>
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<span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s">'maxiter'</span><span class="p">:</span> <span class="mi">500</span><span class="p">})</span>
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<span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">'maxiter'</span><span class="p">:</span> <span class="mi">500</span><span class="p">})</span>
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<span class="c"># Optimization time!</span>
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<span class="c1"># Optimization time!</span>
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<span class="n">display</span><span class="p">(</span><span class="n">optimal</span><span class="o">.</span><span class="n">message</span><span class="p">)</span>
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<span class="n">display</span><span class="p">(</span><span class="s">"Holdings: {}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">tickers</span><span class="p">,</span> <span class="n">optimal</span><span class="o">.</span><span class="n">x</span><span class="p">))))</span>
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<span class="n">display</span><span class="p">(</span><span class="s2">"Holdings: {}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">tickers</span><span class="p">,</span> <span class="n">optimal</span><span class="o">.</span><span class="n">x</span><span class="p">))))</span>
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<span class="n">expected_return</span> <span class="o">=</span> <span class="n">optimal</span><span class="o">.</span><span class="n">fun</span> <span class="o">*</span> <span class="o">-</span><span class="mi">100</span> <span class="c"># multiply by -100 to scale, and compensate for minimizing</span>
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<span class="n">display</span><span class="p">(</span><span class="s">"Expected Return: {:.3f}%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">expected_return</span><span class="p">))</span>
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<span class="n">expected_return</span> <span class="o">=</span> <span class="n">optimal</span><span class="o">.</span><span class="n">fun</span> <span class="o">*</span> <span class="o">-</span><span class="mi">100</span> <span class="c1"># multiply by -100 to scale, and compensate for minimizing</span>
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<span class="n">display</span><span class="p">(</span><span class="s2">"Expected Return: {:.3f}%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">expected_return</span><span class="p">))</span>
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<span class="n">expected_drawdown</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">optimal</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">dd_a</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">optimal</span><span class="o">.</span><span class="n">x</span><span class="p">))</span> <span class="o">*</span> <span class="mi">100</span>
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<span class="n">display</span><span class="p">(</span><span class="s">"Expected Max Drawdown: {0:.2f}%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">expected_drawdown</span><span class="p">))</span>
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<span class="n">display</span><span class="p">(</span><span class="s2">"Expected Max Drawdown: {0:.2f}%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">expected_drawdown</span><span class="p">))</span>
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<span class="c"># TODO: Calculate expected Sharpe</span>
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<span class="c1"># TODO: Calculate expected Sharpe</span>
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</pre></div>
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</div>
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