Vortex Indicator Crossover Strategy

Article Strategy library · Author: Chaos

Summary

This daily futures strategy calculates positive and negative Vortex Indicator values from rolling sums of directional movement and true range. It enters long when the positive value crosses above the negative value and exceeds a threshold; it enters short on the reverse crossover when the negative value exceeds that threshold. Positions are closed if price reaches a stop set two ATRs from entry or if the opposing Vortex crossover occurs. The example uses a fixed contract quantity rather than calculating position size from risk.

The script specifies a fourteen period Vortex calculation, a fourteen period ATR, and a backtest on a Chinese futures contract from November 2022 to April 2023. It reports no performance statistics, so the sample configuration is not evidence of effectiveness. The code also relies on manually tracked direction and entry prices alongside an order management task; these states may diverge from actual fills. Its sample credentials are placeholders, and the symbol comment does not match the contract identifier, so the instrument should be verified before use.

Key ideas

The Vortex Indicator compares rolling positive and negative directional movement with true range.
A crossover filtered by a minimum indicator value triggers a directional entry.
The strategy uses an ATR multiple for stop placement and exits on an opposing crossover.
The example uses a fixed position quantity and daily bars for a specified futures test window.
No performance results are reported, and tracked position state should be checked against actual fills.

Tags

Futures Trend following Technical indicators Risk management Backtesting

Full text

# vortex-indicator


# vortex-indicator









## Source (Apache-2.0)

```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Chaos"

from datetime import date
import numpy as np
import pandas as pd
from tqsdk import TqApi, TqAuth, TqBacktest, TargetPosTask, BacktestFinished
from tqsdk.ta import ATR

# ===== 全局参数设置 =====
SYMBOL = "CFFEX.IC2306"  # 螺纹钢期货合约
POSITION_SIZE = 30  # 固定交易手数
START_DATE = date(2022, 11, 1)  # 回测开始日期
END_DATE = date(2023, 4, 19)  # 回测结束日期

# 涡旋指标参数
VI_PERIOD = 14  # 涡旋指标周期
ATR_PERIOD = 14  # ATR指标周期
ATR_MULTIPLIER = 2.0  # 止损倍数
VI_THRESHOLD = 1.0  # VI值阈值，筛选强度较大的信号

# ===== 全局变量 =====
current_direction = 0   # 当前持仓方向：1=多头，-1=空头，0=空仓
entry_price = 0         # 开仓价格
stop_loss_price = 0     # 止损价格

# ===== 涡旋指标计算函数 =====
def calculate_vortex(df, period=14):
    """计算涡旋指标"""
    # 计算真实范围(TR)
    df['tr'] = np.maximum(
        np.maximum(
            df['high'] - df['low'],
            np.abs(df['high'] - df['close'].shift(1))
        ),
        np.abs(df['low'] - df['close'].shift(1))
    )

    # 计算正向涡旋运动(+VM)
    df['plus_vm'] = np.abs(df['high'] - df['low'].shift(1))

    # 计算负向涡旋运动(-VM)
    df['minus_vm'] = np.abs(df['low'] - df['high'].shift(1))

    # 计算N周期内的总和
    df['tr_sum'] = df['tr'].rolling(window=period).sum()
    df['plus_vm_sum'] = df['plus_vm'].rolling(window=period).sum()
    df['minus_vm_sum'] = df['minus_vm'].rolling(window=period).sum()

    # 计算涡旋指标
    df['plus_vi'] = df['plus_vm_sum'] / df['tr_sum']
    df['minus_vi'] = df['minus_vm_sum'] / df['tr_sum']

    return df

# ===== 策略开始 =====
print("开始运行涡旋指标(VI)期货策略...")

# 创建API实例
api = TqApi(backtest=TqBacktest(start_dt=START_DATE, end_dt=END_DATE),
            auth=TqAuth("快期账户", "快期密码"))

# 订阅合约的日K线数据
klines = api.get_kline_serial(SYMBOL, 60 * 60 * 24)  # 日线数据

# 创建目标持仓任务
target_pos = TargetPosTask(api, SYMBOL)

try:
    while True:
        # 等待更新
        api.wait_update()

        # 如果K线有更新
        if api.is_changing(klines.iloc[-1], "datetime"):
            # 确保有足够的数据计算指标
            if len(klines) < max(VI_PERIOD, ATR_PERIOD) + 5:
                continue

            # 计算涡旋指标
            df = pd.DataFrame(klines)
            df = calculate_vortex(df, VI_PERIOD)

            # 计算ATR
            atr_data = ATR(klines, ATR_PERIOD)
            current_atr = float(atr_data.atr.iloc[-1])

            # 获取最新和前一个周期的数据
            current_price = float(klines.close.iloc[-1])
            current_plus_vi = float(df.plus_vi.iloc[-1])
            current_minus_vi = float(df.minus_vi.iloc[-1])

            prev_plus_vi = float(df.plus_vi.iloc[-2])
            prev_minus_vi = float(df.minus_vi.iloc[-2])

            # 获取当前日期
            current_datetime = pd.to_datetime(klines.datetime.iloc[-1], unit='ns')
            date_str = current_datetime.strftime('%Y-%m-%d')

            # 输出调试信息
            print(f"日期: {date_str}, 价格: {current_price}, +VI: {current_plus_vi:.4f}, -VI: {current_minus_vi:.4f}, ATR: {current_atr:.2f}")

            # ===== 交易逻辑 =====

            # 空仓状态 - 寻找开仓机会
            if current_direction == 0:
                # 多头信号: +VI上穿-VI且+VI > 阈值
                if prev_plus_vi <= prev_minus_vi and current_plus_vi > current_minus_vi and current_plus_vi > VI_THRESHOLD:
                    # 设置入场价格
                    entry_price = current_price

                    # 设置止损价格
                    stop_loss_price = entry_price - ATR_MULTIPLIER * current_atr

                    # 设置持仓方向和目标持仓
                    current_direction = 1
                    target_pos.set_target_volume(POSITION_SIZE)

                    print(f"多头开仓: 价格={entry_price}, 手数={POSITION_SIZE}, 止损价={stop_loss_price:.2f}")

                # 空头信号: -VI上穿+VI且-VI > 阈值
                elif prev_minus_vi <= prev_plus_vi and current_minus_vi > current_plus_vi and current_minus_vi > VI_THRESHOLD:
                    # 设置入场价格
                    entry_price = current_price

                    # 设置止损价格
                    stop_loss_price = entry_price + ATR_MULTIPLIER * current_atr

                    # 设置持仓方向和目标持仓
                    current_direction = -1
                    target_pos.set_target_volume(-POSITION_SIZE)

                    print(f"空头开仓: 价格={entry_price}, 手数={POSITION_SIZE}, 止损价={stop_loss_price:.2f}")

            # 多头持仓 - 检查平仓条件
            elif current_direction == 1:
                # 条件1: 止损触发
                if current_price <= stop_loss_price:
                    profit_pct = (current_price - entry_price) / entry_price * 100
                    target_pos.set_target_volume(0)
                    current_direction = 0
                    print(f"多头止损平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%")

                # 条件2: 信号反转 (-VI上穿+VI)
                elif prev_minus_vi <= prev_plus_vi and current_minus_vi > current_plus_vi:
                    profit_pct = (current_price - entry_price) / entry_price * 100
                    target_pos.set_target_volume(0)
                    current_direction = 0
                    print(f"多头信号平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%")

            # 空头持仓 - 检查平仓条件
            elif current_direction == -1:
                # 条件1: 止损触发
                if current_price >= stop_loss_price:
                    profit_pct = (entry_price - current_price) / entry_price * 100
                    target_pos.set_target_volume(0)
                    current_direction = 0
                    print(f"空头止损平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%")

                # 条件2: 信号反转 (+VI上穿-VI)
                elif prev_plus_vi <= prev_minus_vi and current_plus_vi > current_minus_vi:
                    profit_pct = (entry_price - current_price) / entry_price * 100
                    target_pos.set_target_volume(0)
                    current_direction = 0
                    print(f"空头信号平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%")

except BacktestFinished as e:
    print(f"策略运行异常: {e}")
    api.close()

```

Shown in full with attribution under the source's licence. Licence: Apache-2.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

Vortex Indicator Crossover Strategy with ATR Stops

Summary

Key ideas

Tags