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VibeTrading

Generate complete, validated Python trading strategy code for Hyperliquid from natural language prompts, with error handling, logging, and API integration.

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Generate complete, validated Python trading strategy code for Hyperliquid from natural language prompts, with error handling, logging, and API integration.

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Download the package from Yavira.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/api-details.md

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: crabbytt
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 15 sections Open source page

vibetrading

Agent-first crypto trading framework. Strategies are Python functions decorated with @vibe that call sandbox functions (get_perp_price, long, short, etc.). Same code runs in backtest and live.

Install

pip install vibetrading # Core pip install "vibetrading[hyperliquid]" # + Hyperliquid live trading pip install "vibetrading[dev]" # + pytest, ruff

1. Write a Strategy

import math from vibetrading import vibe, get_perp_price, get_perp_position, get_perp_summary from vibetrading import set_leverage, long, reduce_position, get_futures_ohlcv from vibetrading.indicators import rsi @vibe(interval="1h") def my_strategy(): price = get_perp_price("BTC") if math.isnan(price): return position = get_perp_position("BTC") if position and position.get("size", 0) != 0: pnl = (price - position["entry_price"]) / position["entry_price"] if pnl >= 0.03 or pnl <= -0.02: reduce_position("BTC", abs(position["size"])) return ohlcv = get_futures_ohlcv("BTC", "1h", 20) if ohlcv is None or len(ohlcv) < 15: return if rsi(ohlcv["close"]).iloc[-1] < 30: summary = get_perp_summary() margin = summary.get("available_margin", 0) if margin > 100: set_leverage("BTC", 3) qty = (margin * 0.1 * 3) / price if qty * price >= 15: long("BTC", qty, price, order_type="market")

2. Backtest

import vibetrading.backtest results = vibetrading.backtest.run(code, interval="1h", slippage_bps=5) m = results["metrics"] # Keys: total_return, sharpe_ratio, sortino_ratio, calmar_ratio, max_drawdown, # win_rate, profit_factor, expectancy, number_of_trades, cagr, etc.

3. Deploy Live

import vibetrading.live await vibetrading.live.start( code, exchange="hyperliquid", api_key="0xWalletAddress", api_secret="0xPrivateKey", interval="1m", )

Strategy Rules

Every strategy must: Import and use @vibe or @vibe(interval="1h") decorator Guard against math.isnan(price) — prices are NaN before data loads Check position before entering (avoid stacking) Have both take-profit and stop-loss exits Check margin > 50 and qty * price >= 15 before trading Order types: "market" (fills immediately + slippage) or "limit" (fills at price).

Sandbox Functions

Data: get_perp_price(asset), get_spot_price(asset), get_futures_ohlcv(asset, interval, limit), get_spot_ohlcv(asset, interval, limit), get_funding_rate(asset), get_open_interest(asset), get_current_time(), get_supported_assets() Account: get_perp_summary() → {available_margin, total_margin, ...}, get_perp_position(asset) → {size, entry_price, pnl, leverage} or None, my_spot_balance(asset?), my_futures_balance() Trading: long(asset, qty, price, order_type="market"), short(asset, qty, price, order_type="market"), buy(asset, qty, price), sell(asset, qty, price), reduce_position(asset, qty), set_leverage(asset, leverage)

Indicators

from vibetrading.indicators import sma, ema, rsi, bbands, atr, macd, stochastic, vwap All take pandas Series, return pandas Series. Pure pandas — no dependencies. FunctionSignatureReturnsrsirsi(close, period=14)Series (0-100)bbandsbbands(close, period=20, std=2.0)(upper, middle, lower)macdmacd(close, fast=12, slow=26, signal=9)(macd_line, signal, histogram)atratr(high, low, close, period=14)Seriesstochasticstochastic(high, low, close, k=14, d=3)(%K, %D)

Position Sizing

from vibetrading.sizing import kelly_size, fixed_fraction_size, volatility_adjusted_size, risk_per_trade_size kelly_size(win_rate, avg_win, avg_loss, balance, fraction=0.5) — half-Kelly default risk_per_trade_size(balance, risk_pct, stop_distance, price) — risk-based

Templates

from vibetrading.templates import momentum, mean_reversion, grid, dca, multi_momentum code = momentum() # Returns valid strategy code string

AI Generation

import vibetrading.strategy code = vibetrading.strategy.generate("BTC RSI oversold entry, 3x leverage", model="claude-sonnet-4-20250514") result = vibetrading.strategy.validate(code) # Static analysis report = vibetrading.strategy.analyze(results, strategy_code=code) # LLM analysis Requires ANTHROPIC_API_KEY or OPENAI_API_KEY in environment.

Comparing Strategies

import vibetrading.compare results = vibetrading.compare.run({"RSI": code1, "MACD": code2}, slippage_bps=5) vibetrading.compare.print_table(results) df = vibetrading.compare.to_dataframe(results)

Data Download

import vibetrading.tools from datetime import datetime, timezone data = vibetrading.tools.download_data( ["BTC", "ETH", "SOL"], exchange="binance", interval="1h", start_time=datetime(2025, 1, 1, tzinfo=timezone.utc), end_time=datetime(2025, 6, 1, tzinfo=timezone.utc), ) results = vibetrading.backtest.run(code, data=data, slippage_bps=5)

Exchange Credentials

Store in .env.local (gitignored): Exchangeapi_keyapi_secretExtraHyperliquidWallet address 0x...Private key 0x...—ParadexStarkNet public keyStarkNet private keyaccount_address=LighterAPI keyAPI secret—AsterAPI keyAPI secretuser_address=

Common Patterns

For detailed API docs, strategy patterns, and exchange-specific setup: see references/api-details.md.

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs

SKILL.md Primary doc
references/api-details.md Docs