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<!DOCTYPE html>
<html lang="sv">
<head>
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<title>Om projektet — PPM Kollen</title>
<meta name="description"
content="Bakgrunden och metodiken bakom PPM Kollen — ett hobbyprojekt som använder maskininlärning för att ranka PPM-fonder.">
<meta property="og:title" content="Om PPM Kollen — Bakgrund & Metodik" />
<meta property="og:description"
content="Hobbyproject som kombinerar LSTM, TCN, GRU och XGBoost för att prognostisera PPM-fondavkastning." />
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<a href="#main-content" class="skip-link">Hoppa till innehåll</a>
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<span class="disclaimer-banner-icon" aria-hidden="true">⚠</span>
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<strong data-lang="sv">Inte finansiell rådgivning.</strong>
<strong data-lang="en">Not financial advice.</strong>
<span data-lang="sv"> Prognoserna är experimentella och baserade på historiska mönster —
ingen garanti för framtida avkastning. Konsultera alltid en behörig rådgivare.</span>
<span data-lang="en"> Forecasts are experimental and based on historical patterns — no guarantee of future
returns. Always consult a qualified adviser.</span>
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<span class="logo-title">PPM Kollen</span>
<span class="logo-sub">Fund Forecast</span>
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<!-- ═══ Main ═════════════════════════════════════════════════════ -->
<main id="main-content">
<!-- ── Left column ─────────────────────────────────────────── -->
<div class="main-col">
<!-- Hero -->
<section class="hero">
<div class="hero-eyebrow" data-lang="sv">Om projektet · Bakgrund & Metodik</div>
<div class="hero-eyebrow" data-lang="en">About · Background & Methodology</div>
<h1 class="hero-title">
<span data-lang="sv">Ett hobbyproject om <em>maskininlärning</em> och PPM-fonder</span>
<span data-lang="en">A hobby project on <em>machine learning</em> and pension funds</span>
</h1>
<p class="hero-lede" data-lang="sv">PPM Kollen startade som ett sätt att utforska om moderna ML-modeller kan
hitta mönster i historisk fonddata — och om dessa mönster håller över tid. Det är inte ett finansiellt
verktyg, utan ett lärande- och experimenteringsprojekt.</p>
<p class="hero-lede" data-lang="en">PPM Kollen began as a way to explore whether modern ML models can find
patterns in historical fund data — and whether those patterns hold over time. It is not a financial tool, but
a learning and experimentation project.</p>
</section>
<!-- Disclaimer -->
<div class="disclaimer-card">
<span class="disclaimer-icon">⚠</span>
<div class="disclaimer-text">
<strong data-lang="sv">Inte finansiell rådgivning.</strong>
<strong data-lang="en">Not financial advice.</strong>
<span data-lang="sv"> Prognoserna är experimentella och baserade på historiska mönster som inte garanterar
framtida avkastning. Gör alltid din egen analys och konsultera en behörig rådgivare innan du fattar
investeringsbeslut.</span>
<span data-lang="en"> Forecasts are experimental and based on historical patterns that do not guarantee future
returns. Always do your own analysis and consult a qualified advisor before making investment
decisions.</span>
</div>
</div>
<!-- Bakgrund -->
<section class="section">
<div class="section-label" data-lang="sv">Bakgrund</div>
<div class="section-label" data-lang="en">Background</div>
<h2 class="section-title" data-lang="sv">Varför bygga detta?</h2>
<h2 class="section-title" data-lang="en">Why build this?</h2>
<div class="prose">
<p data-lang="sv">PPM-systemet (Premiepensionsmyndigheten) ger svenska medborgare möjlighet att välja var en
del av deras allmänna pension placeras. Med hundratals fonder att välja mellan är det svårt att orientera
sig utan djupgående analys.</p>
<p data-lang="en">The PPM system (Swedish Premium Pension Authority) allows Swedish citizens to choose where
part of their public pension is invested. With hundreds of funds to choose from, it is hard to navigate
without in-depth analysis.</p>
<p data-lang="sv">Projektet undersöker om <strong>tidsseriemodeller</strong> tränade på veckovis
fondavkastning kan generera meningsfulla prognosintervall — inte exakta prispunkter, utan sannolikhetsband
som hjälper till att förstå osäkerheten i en prognos.</p>
<p data-lang="en">The project investigates whether <strong>time series models</strong> trained on weekly fund
returns can generate meaningful forecast intervals — not exact price points, but probability bands that help
understand the uncertainty in a forecast.</p>
<p data-lang="sv">Allt körs autonomt på en <strong>Jetson-enhet</strong> hemma: modellerna tränas om varje
vecka, resultaten publiceras automatiskt, och systemet är designat att överleva omstarter och
nätverksavbrott.</p>
<p data-lang="en">Everything runs autonomously on a <strong>Jetson device</strong> at home: models are
retrained every week, results are published automatically, and the system is designed to survive reboots and
network interruptions.</p>
</div>
</section>
<!-- Pipeline -->
<section class="section">
<div class="section-label" data-lang="sv">Hur det fungerar</div>
<div class="section-label" data-lang="en">How it works</div>
<h2 class="section-title" data-lang="sv">Pipeline — från rådata till ranking</h2>
<h2 class="section-title" data-lang="en">Pipeline — from raw data to ranking</h2>
<div class="pipeline">
<div class="pipeline-step">
<div class="step-num">01</div>
<div class="step-body">
<div class="step-title" data-lang="sv">Datainsamling</div>
<div class="step-title" data-lang="en">Data collection</div>
<div class="step-desc" data-lang="sv">Veckovis fondavkastning hämtas och normaliseras för ~1 100
PPM-fonder. Robust skalning (median/IQR) används för att minska känsligheten för extremvärden.</div>
<div class="step-desc" data-lang="en">Weekly fund returns are fetched and normalised for ~1,100 PPM funds.
Robust scaling (median/IQR) is used to reduce sensitivity to outliers.</div>
</div>
</div>
<div class="pipeline-step">
<div class="step-num">02</div>
<div class="step-body">
<div class="step-title" data-lang="sv">Modellträning</div>
<div class="step-title" data-lang="en">Model training</div>
<div class="step-desc" data-lang="sv">LSTM-, GRU-, TCN- och XGBoost-modeller tränas per fond och
investeringshorisont — totalt ~4 400 kombinationer per modelltyp. Träningen körs autonomt varje vecka på
lokal hårdvara med checkpoint-baserad kraschåterställning.</div>
<div class="step-desc" data-lang="en">LSTM, GRU, TCN and XGBoost models are trained per fund and
investment horizon — ~4,400 combinations per model type. Training runs autonomously every week on local
hardware with checkpoint-based crash recovery.</div>
</div>
</div>
<div class="pipeline-step">
<div class="step-num">03</div>
<div class="step-body">
<div class="step-title" data-lang="sv">Kvantilprognoser</div>
<div class="step-title" data-lang="en">Quantile forecasting</div>
<div class="step-desc" data-lang="sv">Varje modell genererar tre prognoser: Q10 (pessimistiskt), Q50
(median) och Q90 (optimistiskt). Dessa kombineras till ett ensemble-intervall som visar spridningen i
osäkerhet.</div>
<div class="step-desc" data-lang="en">Each model generates three forecasts: Q10 (pessimistic), Q50
(median), and Q90 (optimistic). These are combined into an ensemble interval showing the spread of
uncertainty.</div>
</div>
</div>
<div class="pipeline-step">
<div class="step-num">04</div>
<div class="step-body">
<div class="step-title" data-lang="sv">Backtest & utvärdering</div>
<div class="step-title" data-lang="en">Backtest & evaluation</div>
<div class="step-desc" data-lang="sv">Modellerna utvärderas på data de inte sett under träning. Nyckeltal
som träffsäkerhet (hit rate), täckning (coverage) och Sharpe-kvot beräknas för att mäta prediktiv
förmåga.</div>
<div class="step-desc" data-lang="en">Models are evaluated on data they have not seen during training. Key
metrics such as hit rate, coverage, and Sharpe ratio are calculated to measure predictive ability.</div>
</div>
</div>
<div class="pipeline-step">
<div class="step-num">05</div>
<div class="step-body">
<div class="step-title" data-lang="sv">Ranking & publicering</div>
<div class="step-title" data-lang="en">Ranking & publishing</div>
<div class="step-desc" data-lang="sv">Fonder rankas efter ett sammansatt poängsystem som väger prognos,
träffsäkerhet och täckning. Resultaten publiceras som JSON och renderas i detta gränssnitt varje vecka.
</div>
<div class="step-desc" data-lang="en">Funds are ranked by a composite scoring system that weights
forecast, hit rate, and coverage. Results are published as JSON and rendered in this interface every
week.</div>
</div>
</div>
</div>
</section>
<!-- Teknisk specifikation -->
<section class="section">
<div class="section-label" data-lang="sv">Teknisk specifikation</div>
<div class="section-label" data-lang="en">Technical specification</div>
<h2 class="section-title" data-lang="sv">Aktuell konfiguration</h2>
<h2 class="section-title" data-lang="en">Current configuration</h2>
<div class="stat-list">
<div class="stat-row">
<span data-lang="sv">Prediktionstyp</span><span data-lang="en">Target mode</span>
<span class="stat-value">Quantile (Q10/Q50/Q90)</span>
</div>
<div class="stat-row">
<span data-lang="sv">Dataskalning</span><span data-lang="en">Scaling</span>
<span class="stat-value">Robust (median/IQR)</span>
</div>
<div class="stat-row">
<span data-lang="sv">Modeller</span><span data-lang="en">Models</span>
<span class="stat-value">LSTM, GRU, TCN, XGBoost</span>
</div>
<div class="stat-row">
<span data-lang="sv">Lookback</span><span data-lang="en">Lookback</span>
<span class="stat-value">104 veckor (2 year)</span>
</div>
<div class="stat-row">
<span data-lang="sv">Finetune-fönster</span><span data-lang="en">Finetune window</span>
<span class="stat-value">8 veckor</span>
</div>
<div class="stat-row">
<span data-lang="sv">Hårdvara</span><span data-lang="en">Hardware</span>
<span class="stat-value">NVIDIA Jetson Orin (8.7 compute)</span>
</div>
</div>
</section>
<!-- Modeller -->
<section class="section">
<div class="section-label" data-lang="sv">Arkitektur</div>
<div class="section-label" data-lang="en">Architecture</div>
<h2 class="section-title" data-lang="sv">Modellerna bakom prognoserna</h2>
<h2 class="section-title" data-lang="en">The models behind the forecasts</h2>
<div class="model-grid">
<div class="model-card">
<div class="model-name">LSTM</div>
<div class="model-full">Long Short-Term Memory</div>
<div class="model-desc" data-lang="sv">Rekurrenta neuronnät med ett inbyggt minne som gör dem skickliga på
att lära sig långsiktiga beroenden i tidsserier. Bildar ryggraden i ensemblen.</div>
<div class="model-desc" data-lang="en">Recurrent neural networks with built-in memory, skilled at learning
long-term dependencies in time series. Forms the backbone of the ensemble.</div>
</div>
<div class="model-card">
<div class="model-name">GRU</div>
<div class="model-full">Gated Recurrent Unit</div>
<div class="model-desc" data-lang="sv">En effektivare variant av LSTM med färre parametrar. Tränas snabbare
och presterar ofta likvärdigt — särskilt bra när träningsdatan är begränsad.</div>
<div class="model-desc" data-lang="en">A more efficient variant of LSTM with fewer parameters. Trains faster
and often performs comparably — particularly strong when training data is limited.</div>
</div>
<div class="model-card">
<div class="model-name">TCN</div>
<div class="model-full">Temporal Convolutional Network</div>
<div class="model-desc" data-lang="sv">Dilaterade kausala konvolutioner som bearbetar sekvenser parallellt
och fångar mönster på flera tidsskalor. Komplementerar de rekurrenta modellerna med kortare, lokala
mönster.</div>
<div class="model-desc" data-lang="en">Dilated causal convolutions that process sequences in parallel and
capture patterns at multiple time scales. Complements the recurrent models with shorter, local patterns.
</div>
</div>
<div class="model-card">
<div class="model-name">XGBoost</div>
<div class="model-full">Extreme Gradient Boosting</div>
<div class="model-desc" data-lang="sv">Gradientförstärkt beslutsträdsensemble. Fångar icke-linjära samband i
tekniska indikatorer och historiska nyckeltal som neurala nätverk ibland missar.</div>
<div class="model-desc" data-lang="en">Gradient-boosted decision tree ensemble. Captures non-linear
relationships in technical indicators and historical metrics that neural networks sometimes miss.</div>
</div>
</div>
<div class="prose">
<p data-lang="sv">Alla fyra modellerna tränas med <strong>kvantilförlust</strong> (pinball loss) för att
producera kalibrerade osäkerhetsintervall (Q10/Q50/Q90) snarare än punktprediktioner. Ensemblen kombinerar
deras utdata — när fler modeller pekar åt samma håll höjs konfidensen i signalen.</p>
<p data-lang="en">All four models are trained with <strong>quantile loss</strong> (pinball loss) to produce
calibrated uncertainty intervals (Q10/Q50/Q90) rather than point predictions. The ensemble combines their
outputs — when more models point in the same direction, confidence in the signal increases.</p>
</div>
</section>
<!-- Horisonter -->
<section class="section">
<div class="section-label" data-lang="sv">Investeringshorisonter</div>
<div class="section-label" data-lang="en">Investment horizons</div>
<h2 class="section-title" data-lang="sv">Fyra tidsskalor, ett beslut</h2>
<h2 class="section-title" data-lang="en">Four time scales, one decision</h2>
<div class="prose">
<p data-lang="sv">Modellen prognostiserar avkastning för fyra investeringshorisonter. Varje horisont tränas
med en separat modell som lär sig mönster på just den tidsskalan.</p>
<p data-lang="en">The model forecasts returns for four investment horizons. Each horizon is trained with a
separate model that learns patterns at that specific time scale.</p>
</div>
<table class="hz-table">
<thead>
<tr>
<th data-lang="sv">Horisont</th>
<th data-lang="sv">Period</th>
<th data-lang="sv">Passar för</th>
<th data-lang="en">Horizon</th>
<th data-lang="en">Period</th>
<th data-lang="en">Suitable for</th>
</tr>
</thead>
<tbody>
<tr>
<td>1w</td>
<td data-lang="sv">1 vecka</td>
<td data-lang="sv">Kortsiktig rörelse, taktisk omplacering</td>
<td data-lang="en">1 week</td>
<td data-lang="en">Short-term movement, tactical reallocation</td>
</tr>
<tr>
<td>4w</td>
<td data-lang="sv">4 veckor</td>
<td data-lang="sv">Månadsvis ombalansering</td>
<td data-lang="en">4 weeks</td>
<td data-lang="en">Monthly rebalancing</td>
</tr>
<tr>
<td>13w</td>
<td data-lang="sv">13 veckor</td>
<td data-lang="sv">Kvartalsvy, säsongsmönster</td>
<td data-lang="en">13 weeks</td>
<td data-lang="en">Quarterly view, seasonal patterns</td>
</tr>
<tr>
<td>26w</td>
<td data-lang="sv">26 veckor</td>
<td data-lang="sv">Halvårsperspektiv, trendföljning</td>
<td data-lang="en">26 weeks</td>
<td data-lang="en">Half-year view, trend following</td>
</tr>
</tbody>
</table>
</section>
<!-- Tech stack -->
<section class="section">
<div class="section-label" data-lang="sv">Teknikstack</div>
<div class="section-label" data-lang="en">Tech stack</div>
<h2 class="section-title" data-lang="sv">Byggt med</h2>
<h2 class="section-title" data-lang="en">Built with</h2>
<div class="prose">
<p data-lang="sv">Projektet kör på en <strong>NVIDIA Jetson</strong>-enhet med 62 GB RAM och NVMe-lagring.
ML-stacken är TensorFlow/Keras med LSTM och TCN. Träningspipelinen är skriven i Python med systemd-tjänster
för schemaläggning och automatisk återstart. Frontendet är ren vanilla HTML/CSS/JS — inga ramverk.</p>
<p data-lang="en">The project runs on an <strong>NVIDIA Jetson</strong> device with 62 GB RAM and NVMe
storage. The ML stack is TensorFlow/Keras with LSTM and TCN. The training pipeline is written in Python with
systemd services for scheduling and automatic restart. The frontend is pure vanilla HTML/CSS/JS — no
frameworks.</p>
</div>
<div class="tag-cloud" style="margin-top:1rem;">
<span class="tag accent">Python</span>
<span class="tag accent">TensorFlow / Keras</span>
<span class="tag accent">LSTM</span>
<span class="tag accent">GRU</span>
<span class="tag accent">TCN</span>
<span class="tag accent">XGBoost</span>
<span class="tag">NVIDIA Jetson</span>
<span class="tag">systemd</span>
<span class="tag">tmux</span>
<span class="tag">JSON / JSONL</span>
<span class="tag">Vanilla JS</span>
<span class="tag">GitHub Pages</span>
</div>
</section>
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<!-- ── Aside column ─────────────────────────────────────────── -->
<aside class="aside-col">
<div class="aside-card">
<div class="aside-card-title" data-lang="sv">Projektfakta</div>
<div class="aside-card-title" data-lang="en">Project facts</div>
<div class="stat-list">
<div class="stat-row">
<span class="stat-label" data-lang="sv">Antal fonder</span>
<span class="stat-label" data-lang="en">Number of funds</span>
<span class="stat-value blue">~1 100</span>
</div>
<div class="stat-row">
<span class="stat-label" data-lang="sv">Modellkombinationer</span>
<span class="stat-label" data-lang="en">Model combinations</span>
<span class="stat-value blue">~4 400</span>
</div>
<div class="stat-row">
<span class="stat-label" data-lang="sv">Horisonter</span>
<span class="stat-label" data-lang="en">Horizons</span>
<span class="stat-value">4</span>
</div>
<div class="stat-row">
<span class="stat-label" data-lang="sv">Modelltyper</span>
<span class="stat-label" data-lang="en">Model types</span>
<span class="stat-value" style="font-size:0.72rem;text-align:right;white-space:normal;max-width:55%">LSTM ·
GRU<br>TCN · XGBoost</span>
</div>
<div class="stat-row">
<span class="stat-label" data-lang="sv">Uppdateringsfrekvens</span>
<span class="stat-label" data-lang="en">Update frequency</span>
<span class="stat-value green" data-lang="sv">Varje vecka</span>
<span class="stat-value green" data-lang="en">Every week</span>
</div>
<div class="stat-row">
<span class="stat-label" data-lang="sv">Hårdvara</span>
<span class="stat-label" data-lang="en">Hardware</span>
<span class="stat-value">Jetson · 62 GB</span>
</div>
</div>
</div>
<div class="aside-card">
<div class="aside-card-title" data-lang="sv">Ordlista — nyckeltal</div>
<div class="aside-card-title" data-lang="en">Glossary — key metrics</div>
<div class="stat-list">
<div>
<div class="stat-label" style="margin-bottom:2px;">Hit rate</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="sv">Andel veckor där
modellen förutspådde rätt riktning (upp/ned). ≥70% = bra.</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="en">Share of weeks where
the model predicted the correct direction (up/down). ≥70% = good.</div>
</div>
<div>
<div class="stat-label" style="margin-bottom:2px;">Coverage</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="sv">Andel veckor det
verkliga utfallet föll inom Q10–Q90-bandet. Lågt = intervallen är för smala.</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="en">Share of weeks the
actual outcome fell within the Q10–Q90 band. Low = intervals are too narrow.</div>
</div>
<div>
<div class="stat-label" style="margin-bottom:2px;">MAE Q50</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="sv">Medelabsolutfel för
medianprognosen. Lägre = noggrannare.</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="en">Mean absolute error
for the median forecast. Lower = more accurate.</div>
</div>
<div>
<div class="stat-label" style="margin-bottom:2px;">Score</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="sv">Sammansatt poäng 0–1.
Väger prognos, träffsäkerhet och täckning.</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="en">Composite score 0–1.
Weights forecast, hit rate, and coverage.</div>
</div>
<div>
<div class="stat-label" style="margin-bottom:2px;">Sharpe</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="sv">Avkastning per
riskenhet i backtestet. ≥1,0 = bra, ≥0,5 = OK.</div>
<div style="font-size:0.8rem;color:var(--text-muted);line-height:1.5;" data-lang="en">Return per unit of
risk in the backtest. ≥1.0 = good, ≥0.5 = OK.</div>
</div>
</div>
</div>
<div class="aside-card">
<div class="aside-card-title" data-lang="sv">Projekthistorik</div>
<div class="aside-card-title" data-lang="en">Project history</div>
<div class="timeline">
<div class="tl-item">
<div class="tl-node">2024</div>
<div class="tl-body">
<div class="tl-title"><span data-lang="sv">Första prototyp</span><span data-lang="en">First
prototype</span></div>
<div class="tl-desc"><span data-lang="sv">LSTM-modell tränad på ett fåtal fonder. Manuell
körning.</span><span data-lang="en">LSTM model trained on a handful of funds. Manual execution.</span>
</div>
</div>
</div>
<div class="tl-item">
<div class="tl-node">Q1 2025</div>
<div class="tl-body">
<div class="tl-title"><span data-lang="sv">Skalning till ~1 100 fonder</span><span data-lang="en">Scaling
to ~1,100 funds</span></div>
<div class="tl-desc"><span data-lang="sv">OOM-problem lösta med streaming JSONL. TCN
introducerades.</span><span data-lang="en">OOM issues solved with streaming JSONL. TCN
introduced.</span></div>
</div>
</div>
<div class="tl-item">
<div class="tl-node">Q2 2025</div>
<div class="tl-body">
<div class="tl-title"><span data-lang="sv">Autonom drift</span><span data-lang="en">Autonomous
operation</span></div>
<div class="tl-desc"><span data-lang="sv">Systemd-tjänster, kraschåterställning och veckovis
publicering.</span><span data-lang="en">Systemd services, crash recovery and weekly publishing.</span>
</div>
</div>
</div>
<div class="tl-item">
<div class="tl-node">Q3 2025</div>
<div class="tl-body">
<div class="tl-title"><span data-lang="sv">Backtest & ranking</span><span data-lang="en">Backtest
& ranking</span></div>
<div class="tl-desc"><span data-lang="sv">Utökat rankningsystem med flera matriser och
backtestblock.</span><span data-lang="en">Extended ranking system with multiple matrices and backtest
blocks.</span></div>
</div>
</div>
<div class="tl-item">
<div class="tl-node"><span data-lang="sv">Nu</span><span data-lang="en">Now</span></div>
<div class="tl-body">
<div class="tl-title"><span data-lang="sv">Pågående</span><span data-lang="en">Ongoing</span></div>
<div class="tl-desc"><span data-lang="sv">Kontinuerlig förfining av modeller och gränssnitt.</span><span
data-lang="en">Continuous refinement of models and interface.</span></div>
</div>
</div>
</div>
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