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Bollinger mean reversion

Rank 8 · Historical simulation

Test
Strategy source

Public repository

Pinned source
Pinned commit source
Repository
https://github.com/duolaAmengweb3/clawquant-trader
Commit
942d9cd326ea7a5ec9a033ef7bf6e615faafa4c7
Source path
clawquant/strategies_builtin/bollinger_bands.py
Source SHA-256
1e04bdcec2b71f47fecaaba05435224979b8289cb10d26f960fccbd5f08f259d
License
MIT · License file
Fixed strategy

How it works

Buy when close is at or below the lower Bollinger band; liquidate when close is at or above the upper band.

Rule settings
Fixed to the tested strategy
Decision interval
1h
Markets
BTC, ETH, SOL
Simulated net returns

Recent simulated results

1 day · base costs

-0.462%

2026-09-082026-09-09

Gross return
-0.451%
Funding
-0.010%
Trading costs
-0.000%
Turnover
0.003
Net return
-0.462%

7 days · base costs

+0.961%

2026-09-022026-09-09

Gross return
+2.171%
Funding
-0.094%
Trading costs
-1.116%
Turnover
7.462
Net return
+0.961%

1 day · stress costs

-0.463%

2026-09-082026-09-09

Gross return
-0.452%
Funding
-0.010%
Trading costs
-0.001%
Turnover
0.003
Net return
-0.463%

7 days · stress costs

-0.166%

2026-09-022026-09-09

Gross return
+2.151%
Funding
-0.093%
Trading costs
-2.223%
Turnover
7.468
Net return
-0.166%

Simulated results do not predict future results.