Quant-Invest-Lab

Unverified ML strategy on Multi by BaptisteZloch. BotFinder score 18 out of 100.

Quant Invest Lab is a project aimed to provide a set of basic tools for quantitative experiments. By quantitative experiment I mean trying to build your own set of investments solu

Source: github

Explorer/Multi/Quant-Invest-Lab
18
Data index
MultiMLMedium risk⚠ Unverified

Quant-Invest-Lab

BaptisteZlochGitHub
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Net return
—
Max drawdown
—
Sharpe
—
Profit factor
—
Win rate
—
Track record
3.4y
BotFinder Analysis

BotFinder analysis pending.

About

Quant-Invest-Lab

Quant Invest Lab Quant Invest Lab is a project aimed to provide a set of basic tools for quantitative experiments. By quantitative experiment I mean trying to build you own set of investments solution. The project is still in its early stage, but I hope it will grow in the future. Initially this project was aimed to be a set of tools for my own experiments, but I decided to make it open source. Of courses it already exists some awesome packages, more detailed, better suited for some use cases. But I hope it will be useful for someone else (learn, practice, understand and create). Feel free to use it, modify it and contribute to it. This package is basically the package I wanted to find when I started to learn quantitative finance. Main features - Data: download data from external data provider without restriction on candle stick, the main provider is kucoin for now (currently only crypto data are supported). - Backtesting: backtest your trading strategy (Long only for now but soon short and leverage) on historical data for different timeframe. Optimize you take profit, stop loss. Acc

PythonMITOpen-sourcebacktestingdata-sciencequantitative-financequantitative-tradingsolutionsstatistics
Track record

⚠ No verified equity curve — no track-record source connected.

Risk

Drawdown profile

Data unavailable — contact the owner.

Evidence

Verification ledger

Live-audited
Broker-verified
Capital-backed
Tamper-proof
Historical evolution

How the score has moved

Recalculated at each data collection. Transparency means showing the bad weeks too.

No score history is stored yet — only the current score is shown.

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Live monitoring

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Alerts on changes: coming soon

Prop-firm compatibility

Prop-firm compatibility not provided.

Source
BaptisteZloch
Since 2016 · 1 bots

Open-source maintainer on GitHub.

Trust 0Profile
Score & reliability18/100
Perf data0/35
Community0/25
Evidence8/20
Recency10/10
Verification0/10

Data-completeness & trust index (not a profitability rating)

Source facts
Stars30
Forks3
Open issues0
LanguagePython
LicenseMIT
Last update2024-01-28
Created2023-05-10
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