vnpy-export

Overview

This skill translates a Vibe-Trading 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 Vibe-Trading strategy inside vnpy's CTA backtester or live trading engine.

Workflow: Export from Backtest Run

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

Workflow: Generate from Description

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

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.

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.

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_ctastrategy import (
    CtaTemplate,
    StopOrder,
    TickData,
    BarData,
    TradeData,
    OrderData,
    BarGenerator,
    ArrayManager,
)


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

    author = "Vibe-Trading"

    # ── 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):
        self.write_log("Strategy stopped")

    def on_tick(self, tick: TickData):
        self.bg.update_tick(tick)

    def on_bar(self, bar: BarData):
        self.cancel_all()

        am = self.am
        am.update_bar(bar)
        if not am.inited:
            return

        # ── INDICATOR CALCULATIONS ──────────────────────────────────────────
        # translate indicators from signal_engine.py using the mapping table

        # ── SIGNAL LOGIC ───────────────────────────────────────────────────
        # set cross_over / cross_under (or long_signal / short_signal) here

        # ── ORDER EXECUTION ────────────────────────────────────────────────
        if cross_over:
            if self.pos == 0:
                self.buy(bar.close_price, 1)
            elif self.pos < 0:
                self.cover(bar.close_price, 1)
                self.buy(bar.close_price, 1)
        elif cross_under:
            if self.pos == 0:
                self.short(bar.close_price, 1)
            elif self.pos > 0:
                self.sell(bar.close_price, 1)
                self.short(bar.close_price, 1)

        self.put_event()

    def on_order(self, order: OrderData):
        pass

    def on_trade(self, trade: TradeData):
        self.put_event()

    def on_stop_order(self, stop_order: StopOrder):
        pass

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 (Vibe-Trading / 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

Vibe-Trading 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 Vibe-Trading 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:

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

To run the vnpy backtester:

from vnpy_ctastrategy.backtesting 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()

Quality Checklist

Before saving the output file:

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/

Overview

This skill translates a Vibe-Trading 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).

Workflow: Export from Backtest Run

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

Workflow: Generate from Description

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

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.

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.

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_ctastrategy import (
    CtaTemplate,
    StopOrder,
    TickData,
    BarData,
    TradeData,
    OrderData,
    BarGenerator,
    ArrayManager,
)


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

    author = "Vibe-Trading"

    # ── 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):
        self.write_log("Strategy stopped")

    def on_tick(self, tick: TickData):
        self.bg.update_tick(tick)

    def on_bar(self, bar: BarData):
        self.cancel_all()

        am = self.am
        am.update_bar(bar)
        if not am.inited:
            return

        # ── INDICATOR CALCULATIONS ──────────────────────────────────────────
        # translate indicators from signal_engine.py using the mapping table

        # ── SIGNAL LOGIC ───────────────────────────────────────────────────
        # set cross_over / cross_under (or long_signal / short_signal) here

        # ── ORDER EXECUTION ────────────────────────────────────────────────
        if cross_over:
            if self.pos == 0:
                self.buy(bar.close_price, 1)
            elif self.pos < 0:
                self.cover(bar.close_price, 1)
                self.buy(bar.close_price, 1)
        elif cross_under:
            if self.pos == 0:
                self.short(bar.close_price, 1)
            elif self.pos > 0:
                self.sell(bar.close_price, 1)
                self.short(bar.close_price, 1)

        self.put_event()

    def on_order(self, order: OrderData):
        pass

    def on_trade(self, trade: TradeData):
        self.put_event()

    def on_stop_order(self, stop_order: StopOrder):
        pass

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 (Vibe-Trading / 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

Vibe-Trading 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 Vibe-Trading 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:

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

To run the vnpy backtester:

from vnpy_ctastrategy.backtesting 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()

Quality Checklist

Before saving the output file:

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/

让 AI 帮你安装

Overview

Workflow: Export from Backtest Run

Workflow: Generate from Description

Asset Class Conventions

CtaTemplate Structure

Full Template

Python → ArrayManager Indicator Mapping

Signal → Order Mapping

Multi-Timeframe Strategies

Output File Instructions

Quality Checklist

References

链接

创作者

Overview

Workflow: Export from Backtest Run

Workflow: Generate from Description

Asset Class Conventions

CtaTemplate Structure

Full Template

Python → ArrayManager Indicator Mapping

Signal → Order Mapping

Multi-Timeframe Strategies

Output File Instructions

Quality Checklist

References