Statistical-Arbitrage-Emerging-Markets

Unverified Arbitrage strategy on Multi by miindisponi99. BotFinder score 18 out of 100.

Built a pairs trading strategy in emerging markets using a rolling Kalman-filter beta and spread half-life, with z-score position sizing, and comprehensive back-testing with liquid

Source: github

Explorer/Multi/Statistical-Arbitrage-Emerging-Markets
18
Data index
MultiArbitrageMedium risk⚠ Unverified

Statistical-Arbitrage-Emerging-Markets

miindisponi99GitHub
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Net return
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Max drawdown
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Sharpe
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Profit factor
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Win rate
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Track record
2.2y
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About

Statistical-Arbitrage-Emerging-Markets

Statistical-Arbitrage-Emerging-Markets In this innovative repository, we delve into the application of statistical arbitrage (StatArb) within the dynamic and intricate landscape of Emerging Markets. Acknowledging the distinct challenges and prospects these markets present, marked by their volatility and potential for growth, the investigation employs an extensive dataset that focuses on varied assets from burgeoning economies, such as those in the BRICS nations. The code elucidates how statistical arbitrage could revolutionize investment and trading strategies in these regions, aiming to exploit market inefficiencies. Methodology The methodology is comprehensive, emphasizing cointegration and leveraging advanced tools like the Kalman Filter and the half-life of spread to identify stable, enduring relationships between assets. The approach is further refined by adaptively modifying position sizes based on z-score tiers and market liquidity, enabling efficient risk management and maximizing opportunities in the market. This repo is essential for anyone seeking to harness the potential

Jupyter NotebookApache-2.0Open-sourcebacktestingemerging-marketskalman-filterpairs-tradingquantitative-analysisquantitative-financequantitative-tradingstatistical-arbitrage
Track record

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

Risk

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Verification ledger

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Source
miindisponi99
Since 2021 · 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
Stars19
Forks0
Open issues0
LanguageJupyter Notebook
LicenseApache-2.0
Last update2024-08-10
Created2024-08-10
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