banzhuan
Python SDK for BanZhuan.AI Builder, AI Chat, backtests, research, drafts, and strategy publishing on:
https://hype.banzhuan.ai(Hyperliquid perps)https://edge.banzhuan.ai(EdgeX perps)https://poly.banzhuan.ai(Polymarket prediction markets)https://afp.banzhuan.ai(Autonity AFP)
This package is designed for automation and agent workflows. It uses the same production session token model as the website.
Installation
pip install banzhuan
Authentication
- Open https://banzhuan.ai/credits
- Connect your wallet and sign in
- Copy your session token from the "Session Token (CLI)" card
- Pass that token to the SDK or store it with the CLI
The package does not perform wallet signing. It assumes you already have a valid session token.
banzhuan auth set <your-session-token>
Or use an environment variable:
export BANZHUAN_SESSION_TOKEN=<your-session-token>
Quick Start
from banzhuan import BanzhuanClient, build_perps_risk_config
# exchange can be "hype", "edge", "poly", or "afp"
client = BanzhuanClient(exchange="hype", session_token="your-session-token")
reply = client.builder_chat(
"Build a trend-following strategy for ETH-USD with a stop loss and cooldown",
current_graph=None,
conversation_history=[],
async_mode=True,
builder_run_config={
"market": "ETH-USD",
"lookbackDays": 14,
"initialCapital": 10_000,
},
)
job = client.wait_for_builder_job(reply["jobId"])
result = job["result"]
graph = client.apply_builder_result(None, result)
backtest = client.create_backtest(
strategy_graph=graph,
start_ms=client.days_ago_ms(14),
end_ms=client.now_ms(),
initial_capital=10_000,
risk_config=build_perps_risk_config(10_000, "ETH-USD"),
)
final_backtest = client.wait_for_backtest(backtest["jobId"], include="result")
print(final_backtest["result"]["summary"])
What The SDK Covers
- Builder AI chat:
POST /builder-chat - Builder async job polling:
GET /builder-chat/jobs/:jobId - Chat session persistence:
POST/GET/DELETE /builder-chat/sessions - Strategy drafts:
POST/GET/DELETE /strategies/drafts - Strategy publishing and versioning:
POST /strategies,POST /strategies/:id/versions - Backtests:
POST /backtests,GET /backtests/:id,GET /backtests - Factor-ledger research:
POST /research/factors/generate-previewPOST /research/factorsPOST /research/factors/evaluatePOST /research/factors/evaluate-allGET /research/jobs/:jobIdGET /research/:id
- Market insights:
GET /market-insights,POST /market-insights/generate
Builder Graphs
Builder AI chat returns command deltas, not a fully materialized graph. This package includes a Python port of the production graph mutation logic so you can:
- send a prompt
- receive Builder commands
- apply them locally
- save the graph
- backtest or publish the graph later
CLI
The CLI is intentionally small and focused on session-token workflows.
Save a session token
banzhuan auth set <your-session-token>
Run a Builder prompt against a graph file
banzhuan builder-chat \
--exchange hype \
--message "Create a funding mean-reversion strategy for HYPE-USD" \
--graph-out graph.json
Backtest a graph file
banzhuan backtest \
--exchange hype \
--graph graph.json \
--market HYPE-USD \
--days 14 \
--capital 10000 \
--out backtest.json
Publish a Builder graph
banzhuan publish \
--exchange hype \
--graph graph.json \
--name "ETH Trend" \
--market ETH-USD \
--visibility private
Notes
- A valid session token authenticates you as the wallet that created it.
- Credits and invite-key checks still apply on production endpoints.
- Research uses the current factor-ledger workflow. The legacy sweep-style
/research/runpath is not used. - Builder AI Chat (
builder_chat(),wait_for_builder_job(),apply_builder_result()) works across all four exchanges. - Backtest and strategy publishing methods use perps-specific formats on HYPE/EDGE. For POLY and AFP, use the Python API with exchange-specific payloads or the web UI.
- Live session deployment can still be completed in the web app.
License
MIT
Metadata
Release files for banzhuan 0.2.0
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