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<li class="nav-item" data-bs-level="1"><a href="#decomposition-first-quant-trading-tutorials-with-detime" class="nav-link">Decomposition-First Quant Trading Tutorials with DeTime</a>
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<li class="nav-item" data-bs-level="2"><a href="#strategy-lab-correction-two-concrete-trading-systems" class="nav-link">Strategy-lab correction: two concrete trading systems</a>
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<h1 id="decomposition-first-quant-trading-tutorials-with-detime">Decomposition-First Quant Trading Tutorials with DeTime</h1>
<p>This tutorial sequence rebuilds the quant-trading examples around one thesis:
classic technical strategies are rough, implicit estimates of trend, cycle,
residual deviation, and market participation. DeTime makes those structures
explicit before the strategy layer.</p>
<p>The revised tutorial uses a seven-part path rather than a loose collection of
indicator notebooks.</p>
<p><a href="notebooks/">Open the executed tutorial notebook index</a>
to jump directly between Tutorial 00-07, the strategy lab notebooks, and the
strategy expansion notebooks.</p>
<table>
<thead>
<tr>
<th>Tutorial</th>
<th>Status</th>
<th>Topic</th>
<th>Main DeTime role</th>
</tr>
</thead>
<tbody>
<tr>
<td>00</td>
<td>implemented</td>
<td>roadmap</td>
<td>strategy design map</td>
</tr>
<tr>
<td>01</td>
<td>implemented</td>
<td>market data and feature factory</td>
<td>price + volume decomposition</td>
</tr>
<tr>
<td>02</td>
<td>implemented</td>
<td>moving average, multi-MA and MACD</td>
<td>explicit trend filters with cycle/residual/volume gates</td>
</tr>
<tr>
<td>03</td>
<td>implemented</td>
<td>RSI, Bollinger and residual mean reversion</td>
<td>residual deviation, cycle timing and volume filter</td>
</tr>
<tr>
<td>04</td>
<td>implemented</td>
<td>Turtle/Donchian breakout</td>
<td>trend, cycle, residual overextension and volume confirmation</td>
</tr>
<tr>
<td>05</td>
<td>implemented</td>
<td>pairs trading and stat arb</td>
<td>spread trend drift + spread residual + optional volume news filter</td>
</tr>
<tr>
<td>06</td>
<td>implemented</td>
<td>cross-sectional rotation</td>
<td>trend/cycle/residual/volume decomposition factor score</td>
</tr>
<tr>
<td>07</td>
<td>implemented</td>
<td>native SSA high-return / low-drawdown replay</td>
<td>fast/slow SSA trends, next-open fills, drawdown and monthly trade diagnostics</td>
</tr>
</tbody>
</table>
<h2 id="strategy-lab-correction-two-concrete-trading-systems">Strategy-lab correction: two concrete trading systems</h2>
<p>The clearest entry point is now the strategy lab, not the broad seven-tutorial map.
It implements two complete strategy families:</p>
<ol>
<li><strong>Trend following</strong>: <code>trend_slope</code> and <code>trend_strength</code> create the trading signal; <code>cycle_position</code>, <code>residual_abs_z</code>, and volume decomposition control entry timing and overextension.</li>
<li><strong>Oscillation / residual reversion</strong>: when <code>abs(trend_strength)</code> is small, the strategy trades <code>residual_z</code> around <code>trend + cycle</code>; negative residual buys, positive residual sells or shorts.</li>
</ol>
<p>Run it with:</p>
<pre><code class="language-bash">make strategy-lab
</code></pre>
<p>Main implementation files:</p>
<pre><code class="language-text">examples/quant_trading/strategy_lab.py
examples/quant_trading/scripts/run_strategy_lab.py
examples/notebooks/quant_trading/01_detime_trend_following_strategy_lab.ipynb
examples/notebooks/quant_trading/02_detime_oscillation_reversion_strategy_lab.ipynb
docs/tutorials/quant-trading/two-strategy-families.md
</code></pre>
<p>The script writes strategy stats, order records, round-trip trades, feature coverage, run manifest, and buy/sell charts under <code>examples/quant_trading/reports/strategy_lab/</code>.</p>
<h2 id="implemented-notebooks">Implemented notebooks</h2>
<p>The executable notebooks are in <code>examples/notebooks/quant_trading/</code>:
install the optional notebook and market-data dependencies from
<code>examples/quant_trading/requirements.txt</code>. The rendered documentation publishes
the captured outputs directly at
<code>quant-trading/notebooks/01_market_data_and_decomposition_feature_factory.md</code>
and the companion pages in the same notebook directory.</p>
<table>
<thead>
<tr>
<th>Notebook</th>
<th>What it teaches</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>00_decomposition_first_quant_trading_roadmap.ipynb</code></td>
<td>Why the tutorial is organized around decomposition rather than isolated indicators.</td>
</tr>
<tr>
<td><code>01_market_data_and_decomposition_feature_factory.ipynb</code></td>
<td>OHLCV audit, period estimation, and walk-forward price/volume feature construction.</td>
</tr>
<tr>
<td><code>02_decomposition_aware_moving_average_macd.ipynb</code></td>
<td>Classical buy-and-hold, dual MA, MACD, multi-MA and momentum compared with DeTime rewrites.</td>
</tr>
<tr>
<td><code>03_residual_mean_reversion_rsi_bollinger.ipynb</code></td>
<td>Price z-score, RSI, Bollinger and APO baselines rewritten as residual mean-reversion with cycle timing.</td>
</tr>
<tr>
<td><code>04_turtle_donchian_breakout_volume_confirmation.ipynb</code></td>
<td>Donchian/Turtle breakout rewritten with trend, cycle, residual and volume confirmation.</td>
</tr>
<tr>
<td><code>05_pairs_spread_decomposition_stat_arb.ipynb</code></td>
<td>Pair z-score and rolling-beta spread baselines rewritten as residual spread trading with spread-trend drift control.</td>
</tr>
<tr>
<td><code>06_cross_sectional_rotation_portfolio.ipynb</code></td>
<td>Momentum, multi-MA and inverse-volatility rotation compared with DeTime cross-sectional scoring.</td>
</tr>
<tr>
<td><code>07_native_ssa_high_return_low_drawdown_tutorial.ipynb</code></td>
<td>Native SSA dual-trend mean-reversion replay for selected high-return FX and crypto strategies under a 20% drawdown constraint.</td>
</tr>
</tbody>
</table>
<h2 id="core-design">Core design</h2>
<p>For price and volume we use:</p>
<p>[
\log P_t = T^P_t + C^P_t + R^P_t,
\qquad
\log(1 + V_t) = T^V_t + C^V_t + R^V_t.
]</p>
<p>The strategy layer then reads:</p>
<table>
<thead>
<tr>
<th>Component</th>
<th>Trading interpretation</th>
<th>Example use</th>
</tr>
</thead>
<tbody>
<tr>
<td>price trend</td>
<td>direction and persistence</td>
<td>trend-following state, decomposed MA/MACD</td>
</tr>
<tr>
<td>price cycle</td>
<td>timing and local oscillation</td>
<td>avoid buying into overextended cycle peaks</td>
</tr>
<tr>
<td>price residual</td>
<td>deviation from current structure</td>
<td>pullback and mean-reversion logic</td>
</tr>
<tr>
<td>volume trend</td>
<td>participation</td>
<td>confirm trend or breakout</td>
</tr>
<tr>
<td>volume residual</td>
<td>abnormal activity</td>
<td>detect volume shock or weak participation</td>
</tr>
<tr>
<td>reconstruction error / stability</td>
<td>feature reliability</td>
<td>reduce exposure when component quality weakens</td>
</tr>
</tbody>
</table>
<h2 id="data-layer">Data layer</h2>
<p>The code supports live downloads through <code>yfinance</code>. The rendered documentation
uses archived historical GOOG and FX OHLCV samples from the user-provided Learn
Algorithmic Trading material so the pages can be rebuilt without network access.</p>
<p>For formal trading research, replace the educational data source with a licensed
point-in-time vendor and document symbol membership, corporate actions,
delistings, borrow, funding, FX, and execution assumptions.</p>
<h2 id="code-modules">Code modules</h2>
<table>
<thead>
<tr>
<th>Module</th>
<th>Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>data.py</code></td>
<td>OHLCV download, panel extraction, archived market-data loader</td>
</tr>
<tr>
<td><code>classic_indicators.py</code></td>
<td>SMA, EMA, MACD, RSI, Bollinger, momentum, APO</td>
</tr>
<tr>
<td><code>decomposition_features.py</code></td>
<td>walk-forward price and volume DeTime features</td>
</tr>
<tr>
<td><code>strategy_baselines.py</code></td>
<td>classical baseline weight recipes</td>
</tr>
<tr>
<td><code>strategy_detime.py</code></td>
<td>decomposition-aware strategy recipes</td>
</tr>
<tr>
<td><code>strategy_pairs.py</code></td>
<td>pair spread decomposition and stat-arb recipes</td>
</tr>
<tr>
<td><code>strategy_rotation.py</code></td>
<td>cross-sectional rotation and portfolio recipes</td>
</tr>
<tr>
<td><code>validation.py</code></td>
<td>common backtest comparison, turnover, manifest helpers</td>
</tr>
<tr>
<td><code>backtest.py</code></td>
<td>transparent close-to-close research backtester</td>
</tr>
</tbody>
</table>
<h2 id="smoke-test">Smoke test</h2>
<pre><code class="language-bash">export PYTHONPATH="$PWD/src:$PWD/examples:$PYTHONPATH"
make smoke-05-06
</code></pre>
<p>The smoke tests run on CPU and write hardware/audit outputs under
<code>examples/quant_trading/reports/</code>.</p>
<h2 id="latest-implemented-tutorials">Latest Implemented Tutorials</h2>
<p>Tutorials 03-04 cover single-asset reversion and breakout strategies:</p>
<ul>
<li>Tutorial 03 keeps familiar entry points, including RSI, Bollinger Bands, APO and price z-score, but changes the traded object from raw price deviation to residual deviation after trend/cycle removal.</li>
<li>Tutorial 04 keeps the Donchian/Turtle breakout scaffold but adds trend, cycle, residual-overextension and volume-participation gates.</li>
</ul>
<p>Tutorials 05-07 complete the current arc:</p>
<ul>
<li>Tutorial 05 rewrites pairs trading by decomposing the rolling hedge spread and trading residual deviation only when spread trend drift is controlled.</li>
<li>Tutorial 06 turns decomposition outputs into a cross-sectional factor score for top-N, long-short and volatility-targeted rotation portfolios.</li>
<li>Tutorial 07 replays selected native SSA dual-trend strategies, including buy/sell markers, strategy-vs-buy-hold equity, drawdown curves, and monthly return / win-rate tables.</li>
</ul>
<p>Run the latest batch on bundled real FX samples:</p>
<pre><code class="language-bash">make quant-columns-05-06
</code></pre>
<p>Run the bundled-sample Tutorial 01-06 batch:</p>
<pre><code class="language-bash">make quant-columns-01-06
</code></pre>
<p>Tutorial 07 is a notebook replay for local 3-minute intraday files. Open it from
the rendered documentation or run
<code>examples/notebooks/quant_trading/07_native_ssa_high_return_low_drawdown_tutorial.ipynb</code>
after placing the referenced FX and crypto CSVs under
<code>examples/quant_trading/data/intraday_crypto_fx/</code>.</p>
<p>Live-data versions are available through <code>make quant-columns-03-04-live</code> and <code>make quant-columns-05-06-live</code>.</p>
<h2 id="strategy-expansion-method-specific-variants-and-component-pair-trading">Strategy expansion: method-specific variants and component pair trading</h2>
<p>The strategy lab now includes an additional expansion layer that treats a decomposition method and horizon as part of the strategy definition.</p>
<pre><code class="language-bash">make strategy-expansion
</code></pre>
<p>This command runs:</p>
<ol>
<li>method-specific strategy variants: STL/SSA/STD configurations produce different trend, cycle, and residual inputs for the same trend-following, oscillation-reversion, residual-Bollinger, trend-MACD, and trend-crossover rules;</li>
<li>component-level pair trading: both assets in a pair are decomposed, trend/cycle similarity is measured, cointegration and stationarity diagnostics are reported, and the residual gap or trend+cycle spread deviation is traded.</li>
</ol>
<p>Tutorial document:</p>
<pre><code class="language-text">docs/tutorials/quant-trading/method-specific-strategy-expansion.md
</code></pre>
<p>New notebooks:</p>
<pre><code class="language-text">examples/notebooks/quant_trading/03_detime_method_specific_strategy_variants.ipynb
examples/notebooks/quant_trading/04_detime_component_pair_trading_cointegration.ipynb
</code></pre></div>
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