vnpy-export

name	vnpy-export
description	Export a AStockPursue backtest strategy to a runnable vnpy CtaTemplate Python class — supports A-share equities, futures, and crypto via BarGenerator + ArrayManager.
category	tool

Overview

This skill translates a AStockPursue strategy into a vnpy CtaTemplate subclass .py file that can be loaded directly into the vnpy CTA Strategy App for live trading or vnpy backtesting.

Output file: artifacts/vnpy_strategy/<StrategyName>Strategy.py (inside the run directory).

vnpy is the most widely-used open-source quant framework in mainland China (39k+ GitHub stars). Use this skill when the user asks to export to vnpy, requests a /vnpy command, or wants to run a AStockPursue strategy inside vnpy's CTA backtester or live trading engine.

---

Workflow: Export from Backtest Run

1. load_skill("vnpy-export") — read this guide
2. read_file("config.json") — extract instrument, dates, parameters, interval
3. read_file("code/signal_engine.py") — understand the Python signal logic
4. Determine asset class from config.json → choose correct CtaTemplate convention (see below)
5. Translate signal logic to CtaTemplate using the reference tables
6. write_file("artifacts/vnpy_strategy/<StrategyName>Strategy.py") — save the output
7. Return the class in a code block with setup instructions

Workflow: Generate from Description

1. load_skill("vnpy-export") — read this guide
2. Write a CtaTemplate class from the user's strategy description
3. write_file("artifacts/vnpy_strategy/<StrategyName>Strategy.py") — save the output
4. Return the class with setup and usage instructions

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Asset Class Conventions

vnpy uses the same CtaTemplate base class for all asset types, but parameter conventions differ:

Asset Class	Instrument Example	vt_symbol Format	Position Unit
A-share stock	Ping An Bank	000001.SZSE	shares (整手, min 100)
Futures	IF2406	IF2406.CFFEX	lots
Crypto	BTC/USDT	BTC/USDT.BINANCE	coin units

For stocks: use buy / sell only (no short selling unless margin account). For futures / crypto: use all four directions — buy, sell, short, cover.

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CtaTemplate Structure

Every strategy must subclass CtaTemplate and implement these methods:

Method	Purpose
__init__	Declare parameters, variables, BarGenerator, ArrayManager
on_init	Called once at startup; call load_bar(n) to warm up indicators
on_start	Called when strategy is started by user
on_stop	Called when strategy is stopped
on_tick	Receives live tick data; forward to BarGenerator
on_bar	Main logic — called once per bar by BarGenerator
on_order	Order status updates
on_trade	Fill notifications
on_stop_order	Stop-order status (if using stop orders)

Always call self.cancel_all() at the start of on_bar to avoid stale orders. Always call self.put_event() at the end of on_bar to refresh the UI.

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Full Template

See scripts/cta_template.py for a complete, runnable example (MA crossover). The template below is the canonical skeleton — replace the # SIGNAL LOGIC section:

from vnpy.app.cta_strategy import (
    CtaTemplate,
    StopOrder,
    TickData,
    BarData,
    TradeData,
    OrderData,
    BarGenerator,
    ArrayManager,
)


class {{StrategyName}}Strategy(CtaTemplate):
    """
    AStockPursue export — {{StrategyName}}
    Generated from run: {{run_id}}
    Instrument: {{vt_symbol}}
    """

    author = "AStockPursue"

    # ── Parameters (editable in vnpy UI) ──────────────────────────────────
    {{param_name}} = {{param_default}}   # add one line per parameter

    parameters = [{{param_list_as_strings}}]

    # ── Variables (displayed in vnpy UI, reset on strategy restart) ────────
    {{var_name}} = 0.0   # add one line per runtime variable

    variables = [{{var_list_as_strings}}]

    def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
        super().__init__(cta_engine, strategy_name, vt_symbol, setting)
        self.bg = BarGenerator(self.on_bar)
        self.am = ArrayManager()

        # initialise variable attributes to match class-level defaults
        # (vnpy requires instance attributes for variables declared above)

    def on_init(self):
        self.write_log("Strategy initialised")
        self.load_bar({{warmup_bars}})   # load enough bars to warm up all indicators

    def on_start(self):
        self.write_log("Strategy started")
        self.put_event()

    def on_stop(self):
        .write_log()

     ():
        .bg.update_tick(tick)

     ():
        .cancel_all()

        am = .am
        am.update_bar(bar)
          am.inited:
            

        
        

        
        

        
         cross_over:
             .pos == :
                .buy(bar.close_price, )
             .pos < :
                .cover(bar.close_price, )
                .buy(bar.close_price, )
         cross_under:
             .pos == :
                .short(bar.close_price, )
             .pos > :
                .sell(bar.close_price, )
                .short(bar.close_price, )

        .put_event()

     ():
        

     ():
        .put_event()

     ():
        

---

Python → ArrayManager Indicator Mapping

ArrayManager is vnpy's built-in vectorised indicator library. Always prefer it over pandas when the equivalent method exists — it is faster and avoids look-ahead bias.

Python (AStockPursue / pandas / ta-lib)	vnpy ArrayManager
df['close'].rolling(n).mean()	am.sma(n)
df['close'].ewm(span=n).mean()	am.ema(n)
ta.RSI(close, n)	am.rsi(n)
ta.MACD(close, 12, 26, 9)	am.macd(12, 26, 9) → (macd, signal, hist)
Bollinger Bands	am.boll(n, dev) → (mid, upper, lower)
ATR	am.atr(n)
ADX	am.adx(n)
df['close'].rolling(n).std()	am.std(n)
Stochastic K, D	am.kd(n, m) → (k, d)
df['high'].rolling(n).max()	am.high_array[-n:].max()
df['low'].rolling(n).min()	am.low_array[-n:].min()
Donchian channel	am.donchian(n) → (upper, lower)
df['close'].shift(1) (previous bar)	am.close_array[-2]
Last N bars as array	am.sma(n, array=True) (returns full array)

Using arrays: pass array=True to get the full history array (e.g. for crossover detection):

fast_ma = am.sma(self.fast_window, array=True)
cross_over = fast_ma[-1] > slow_ma[-1] and fast_ma[-2] <= slow_ma[-2]

---

Signal → Order Mapping

AStockPursue signal	Position check	vnpy call
Long entry	self.pos == 0	self.buy(price, volume)
Long entry (reverse from short)	self.pos < 0	self.cover(price, vol); self.buy(price, vol)
Long exit	self.pos > 0	self.sell(price, volume)
Short entry	self.pos == 0	self.short(price, volume)
Short entry (reverse from long)	self.pos > 0	self.sell(price, vol); self.short(price, vol)
Short exit	self.pos < 0	self.cover(price, volume)
Close all (stop signal)	any	self.cancel_all() then sell / cover as needed

Price conventions:

• For backtesting: use bar.close_price (market order equivalent)
• For live trading with limit orders: use bar.close_price ± a small offset (e.g. * 1.001)
• For stop orders: use self.buy_stop(trigger, volume) / self.short_stop(trigger, volume)

Volume conventions:

• Stocks: volume in shares; must be a multiple of 100 (round lots)
• Futures: volume in lots (usually 1 for CtaTemplate strategies)
• Crypto: volume in base-currency units (e.g., BTC)

---

Multi-Timeframe Strategies

When the AStockPursue strategy uses multiple timeframes (e.g., daily signal, hourly entry):

def __init__(self, ...):
    super().__init__(...)
    self.bg = BarGenerator(self.on_bar, 5, self.on_5min_bar)   # 5-min bars
    self.bg_d = BarGenerator(self.on_bar, window=1, on_window_bar=self.on_daily_bar,
                              interval=Interval.DAILY)          # daily bars
    self.am = ArrayManager()
    self.am_d = ArrayManager(size=100)                          # daily ArrayManager

def on_bar(self, bar: BarData):
    self.bg.update_bar(bar)    # feeds 5-min generator

def on_5min_bar(self, bar: BarData):
    self.bg_d.update_bar(bar)  # feeds daily generator
    # put intraday entry logic here

def on_daily_bar(self, bar: BarData):
    self.am_d.update_bar(bar)
    # put daily trend-filter logic here

---

Output File Instructions

Save the generated file to: artifacts/vnpy_strategy/<StrategyName>Strategy.py

To load in vnpy:

1. Copy the file to your vnpy project's strategies/ folder (or any folder on sys.path)
2. Open the vnpy Trader → CTA Strategy App
3. Click Add Strategy → select <StrategyName>Strategy from the dropdown
4. Set vt_symbol (e.g. IF2406.CFFEX) and adjust parameters
5. Click Init → Start to begin

To run the vnpy backtester:

from vnpy.app.cta_backtester import BacktestingEngine
from vnpy.trader.constant import Interval

engine = BacktestingEngine()
engine.set_parameters(
    vt_symbol="000001.SZSE",
    interval=Interval.DAILY,
    start=datetime(2020, 1, 1),
    end=datetime(2024, 1, 1),
    rate=0.0003,
    slippage=0.02,
    size=1,
    pricetick=0.01,
    capital=1_000_000,
)
engine.add_strategy({{StrategyName}}Strategy, {})
engine.load_data()
engine.run_backtesting()
df = engine.calculate_result()
engine.calculate_statistics()
engine.show_chart()

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Quality Checklist

Before saving the output file:

• Class name ends with Strategy and matches the filename
• All parameters entries have matching class-level defaults and __init__ instance attributes
• All variables entries have matching instance attributes initialised in __init__
• on_bar calls self.cancel_all() at the start
• on_bar calls self.put_event() at the end
• on_bar returns early if not am.inited
• Position direction checked with self.pos before every order call
• Stocks: no short / cover calls unless margin trading is explicitly requested
• load_bar(n) warmup in on_init is at least max(all indicator windows) + 2
• Comment block at top of file notes the original AStockPursue run_id and instrument

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References

• vnpy CtaTemplate source: vnpy/app/cta_strategy/template.py
• ArrayManager source: vnpy/app/cta_strategy/base.py
• Official docs: https://www.vnpy.com/docs/cn/cta_strategy.html
• Example strategies (official): vnpy/app/cta_strategy/strategies/