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trend-allocation-research-bot

Faber-style trend-following asset allocation across 7 ETFs: a research backtest (2007–2026) with no look-ahead, transaction costs and robustness/regime analysis, plus a live Alpaca paper-trading bot and a Streamlit dashboard.

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This is 1 of 113 launches in algorithmic and prediction market trading tools — see how it stacks up on momentum and crowding →

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Details

External ID
1404229000
Source
GITHUB
Company
—
Product
trend-allocation-research-bot
Website domain
streamlit.app
Launched
Oct. 4, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.10
Tags
algorithmic-trading, alpaca, asset-allocation, backtesting, python, quantitative-finance, technical-analysis, trading-bot, trading-strategy-simulation

Enrichment

Niche
algorithmic and prediction market trading tools
Vertical
Fintech
Function
Analytics & BI
Audience
Prosumer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
trend-following asset allocation backtesting and paper-trading bot
Manually corrected
False

Could you build this?

Yes Writing a Faber trend-following algorithm using historical ETF data, Alpaca's paper trading API, and a Streamlit dashboard relies entirely on well-documented quantitative Python libraries.

Competitors

Other products that read as similar to this one — 284 launches clear the similarity bar, closest 8 shown.

Attention rank: #268 of 285 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 340 days after the earliest competitor.

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