Volume Price Trend Strategy

Code TqSdk

Summary

This example describes a futures strategy using the Volume Price Trend (VPT) indicator on daily bars. It updates VPT by adding volume multiplied by the latest percentage price change, then compares the current value with a moving average. A trade is considered only when volume exceeds a threshold relative to its recent average and price direction agrees with the VPT signal: rising price and VPT above its average initiate a long, while falling price and VPT below its average initiate a short. Positions are closed when the VPT relationship reverses under elevated volume.

The script sets a fixed contract quantity and uses a target-position interface within a historical test on a gold futures contract. It records entry prices and prints percentage profit or loss when positions close, but the supplied material reports no performance results. The sample has no explicit stop loss, transaction-cost model, or broader risk controls, and its short test window and specific parameters do not establish robustness or live-trading suitability.

Key ideas

VPT accumulates volume scaled by the percentage change in price.
The strategy filters VPT direction signals by requiring unusually high recent volume.
It opens long or short positions when price movement agrees with the VPT level relative to its moving average.
A reversal in the VPT condition with elevated volume triggers position closure.
The example provides no reported performance evidence or detailed risk controls.

Tags

Futures Technical indicators Momentum Backtesting Execution

Full text

# volume_price.py


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

from datetime import date

import numpy as np
from tqsdk import TqApi, TqAuth, TqBacktest, TargetPosTask, BacktestFinished

# ===== 全局参数设置 =====
SYMBOL = "SHFE.au2306"  # 交易合约
POSITION_SIZE = 30  # 持仓手数
START_DATE = date(2023, 1, 15)  # 回测开始日期
END_DATE = date(2023, 5, 15)  # 回测结束日期

# VPT策略参数
VPT_MA_PERIOD = 14  # VPT均线周期
VOLUME_THRESHOLD = 1.5  # 成交量放大倍数阈值

print(f"开始回测 {SYMBOL} 的量价趋势(VPT)策略...")
print(f"参数: VPT均线周期={VPT_MA_PERIOD}, 成交量阈值={VOLUME_THRESHOLD}")

api = None
try:
    api = TqApi(backtest=TqBacktest(start_dt=START_DATE, end_dt=END_DATE),
                auth=TqAuth("快期账户", "快期密码"))

    # 订阅日K线数据
    klines = api.get_kline_serial(SYMBOL, 60 * 60 * 24)
    target_pos = TargetPosTask(api, SYMBOL)

    # 初始化交易状态
    position = 0  # 当前持仓
    entry_price = 0  # 入场价格
    vpt_values = []  # 存储VPT值

    while True:
        api.wait_update()

        if api.is_changing(klines):
            # 确保有足够的数据
            if len(klines) < VPT_MA_PERIOD + 1:
                continue

            # 计算VPT指标
            close = klines.close.values
            volume = klines.volume.values

            # 计算最新的VPT值
            if len(vpt_values) == 0:
                vpt_values.append(volume[-1])
            else:
                price_change_pct = (close[-1] - close[-2]) / close[-2]
                new_vpt = vpt_values[-1] + volume[-1] * price_change_pct
                vpt_values.append(new_vpt)

            # 保持VPT列表长度与K线数据同步
            if len(vpt_values) > len(klines):
                vpt_values.pop(0)

            # 计算VPT均线
            if len(vpt_values) >= VPT_MA_PERIOD:
                vpt_ma = np.mean(vpt_values[-VPT_MA_PERIOD:])

                # 获取当前价格和成交量数据
                current_price = float(close[-1])
                current_volume = float(volume[-1])
                avg_volume = np.mean(volume[-VPT_MA_PERIOD:-1])

                # 判断成交量是否放大
                volume_increased = current_volume > avg_volume * VOLUME_THRESHOLD

                # 交易信号判断
                vpt_trend_up = vpt_values[-1] > vpt_ma
                vpt_trend_down = vpt_values[-1] < vpt_ma

                # 交易逻辑
                if position == 0:  # 空仓
                    if vpt_trend_up and volume_increased and close[-1] > close[-2]:
                        position = POSITION_SIZE
                        entry_price = current_price
                        target_pos.set_target_volume(position)
                        print(f"开多仓: 价格={current_price:.2f}, VPT上穿均线")

                    elif vpt_trend_down and volume_increased and close[-1] < close[-2]:
                        position = -POSITION_SIZE
                        entry_price = current_price
                        target_pos.set_target_volume(position)
                        print(f"开空仓: 价格={current_price:.2f}, VPT下穿均线")

                elif position > 0:  # 持有多头
                    if vpt_trend_down and volume_increased:
                        profit_pct = (current_price / entry_price - 1) * 100
                        target_pos.set_target_volume(0)
                        print(f"平多仓: 价格={current_price:.2f}, 盈亏={profit_pct:.2f}%")
                        position = 0
                        entry_price = 0

                elif position < 0:  # 持有空头
                    if vpt_trend_up and volume_increased:
                        profit_pct = (entry_price / current_price - 1) * 100
                        target_pos.set_target_volume(0)
                        print(f"平空仓: 价格={current_price:.2f}, 盈亏={profit_pct:.2f}%")
                        position = 0
                        entry_price = 0

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.

Volume Price Trend Signals with a Volume Expansion Filter

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