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相对量价策略Relative-Volume-Price-Strategy.md

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Name

相对量价策略Relative-Volume-Price-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

相对量价策略是一种基于交易量异常和价格波动的量化交易策略。该策略通过比较当前交易量与历史平均交易量的关系,判断交易量是否异常;同时结合平均真实波幅区间,判断价格是否处于相对稳定的区间。当交易量异常增加且价格相对稳定时,认为是进入信号。

策略原理

相对量价策略的核心逻辑基于两个指标进行判断,一个是相对交易量,一个是价格波动区间。

首先,我们计算最近 20 周期的交易量的简单移动平均,作为历史平均交易量。然后设置一个倍数参数(如 1.5 倍),当当前交易量大于平均交易量的 1.5 倍时,我们认为交易量异常,属于“相对量”的情况。

其次,我们计算最近 14 周期的平均真实波幅(ATR)作为价格波动的度量。同时计算平均波幅的标准差,如果当前真实波幅位于平均值正负一个标准差之间,我们认为价格波动处于相对平稳的区间。

当上述两个条件同时满足时,即发出做多信号,开仓做多。持仓过程中,以最大低点减去两倍 ATR 作为止损位,以最高点减去两倍 ATR 作为止盈位。

优势分析

相对量价策略最大的优势在于捕捉异常交易量带来的价格趋势。当交易量激增时,代表了市场参与者态度的改变,这常常预示着价格的突破和新趋势的形成。策略通过比较交易量与历史平均值的关系,可有效判断交易量异常的时点。

另一方面,策略还考虑了价格波动率,使得信号发生在价格相对平稳的时段。这避免了在剧烈波动时追高造成的巨大亏损风险。同时也增加了获利机会,因为趋势通常在相对平稳后才开始突破。

风险分析

该策略最大的风险在于交易量指标并不能百分之百确定新的趋势,交易量激增有可能是假突破,价格很快就反转。这种情况下,策略vrfs受到较大损失。

为降低损失,可适当调整“相对量”的参数,对交易量异常的判断设更严格的标准。或者增加其他判断指标,如增加成交额分析,判断交易量增长是否与成交金额匹配。

优化方向

该策略可以从以下几个方面进行优化:

  1. 增加其他指标判断,如涨跌幅,成交额等,使交易量异常信号更加可靠。

  2. ATR 参数可针对不同股票进行优化,更准确判断价格稳定区间。

  3. 增加机器学习算法,对交易量异常进行积极判断,而不仅是与历史平均值简单比较。

  4. 利用深度学习模型判断价格波动预测,而不仅仅是基于历史ATR。

总结

相对量价策略通过捕捉交易量异常作为特征信号,结合价格稳定判断发出交易信号。策略简单实用,在追踪股票异常交易量时效果较好。但也存在一定的假信号风险,需进一步优化指标判断以提高效果。

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Overview

The relative volume price strategy is a quantitative trading strategy based on abnormal trading volume and price volatility. This strategy compares the current trading volume with the historical average to determine if the trading volume is abnormal. It also combines the average true range interval to determine if the price is relatively stable. When trading volume increases abnormally and the price is relatively stable, it is considered an entry signal.

Strategy Principle

The core logic of the relative volume price strategy is based on two indicators for judgment: relative trading volume and price fluctuation range.

First, we calculate the simple moving average of trading volume over the most recent 20 periods as the historical average trading volume. Then we set a multiple parameter (such as 1.5 times). When the current trading volume is greater than 1.5 times the average trading volume, we consider the trading volume to be abnormal and belonging to a "relative volume" situation.

Secondly, we calculate the average true range (ATR) over the most recent 14 periods as a measure of price volatility. At the same time, we calculate the standard deviation of the average volatility. If the current true volatility is between the average plus or minus one standard deviation, we consider the price fluctuation to be in a relatively stable range.

When the above two conditions are met at the same time, a long signal is issued to open a long position. During the holding period, twice the ATR is used as the stop loss level, and the highest price minus twice the ATR is used as the take profit level.

Advantage Analysis

The biggest advantage of the relative volume price strategy is that it captures price trends brought about by abnormal trading volume. When trading volume surges, it represents a change in the attitude of market participants, which often signals price breakouts and the formation of new trends. By comparing the relationship between trading volume and historical averages, the strategy can effectively determine the timing of abnormal trading volume.

On the other hand, the strategy also considers the volatility rate, so that signals occur during relatively stable price periods. This avoids the huge risk of loss caused by chasing highs during violent fluctuations. It also increases profit opportunities because trends usually start breaking through after relative stability.

Risk Analysis

The biggest risk of this strategy is that the trading volume indicator cannot be 100% certain of new trends. Surges in trading volume may be false breakouts and prices quickly reverse. In such cases, the strategy suffers greater losses.

To reduce losses, appropriately adjust the parameters of "relative volume" and set stricter criteria for judging abnormal trading volume. Or add other judgment indicators, such as analyzing the increase in trading volume to see if it matches the increase in turnover.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Add other indicators for judgment, such as change ratio, turnover, etc., to make trading volume abnormal signals more reliable.

  2. The ATR parameter can be optimized for different stocks to more accurately determine the stable price range.

  3. Add machine learning algorithms to actively judge abnormal trading volume, not just simple comparison with historical averages.

  4. Use deep learning models to predict price volatility, not just based on historical ATR.

Conclusion

The relative volume price strategy captures abnormal trading volume as a characteristic signal and combines price stability judgment to issue trading signals. The strategy is simple and practical, and works well in tracking abnormal stock trading volume. But there is also a certain risk of false signals that need to be further optimized by indicator judgments to improve effectiveness.

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Strategy Arguments

Argument Default Description
v_input_1 timestamp(01 Apr 2016 13:30 +0000) Backtest Start Time
v_input_2 false Define backtest end-time (If false, will test up to most recent candle)
v_input_3 timestamp(01 May 2021 19:30 +0000) Backtest End Time (if checked above)
v_input_4 14 Length of ATR to determine volatility
v_input_5 14 Length of ATR for trailing stop loss
v_input_6 2 ATR Multiplier for trailing stop loss
v_input_7 20 SMA(volume) length (for relative comparison)
v_input_8 1.5 Multiple of avg vol to consider relative volume as being high

Source (PineScript)

/*backtest
start: 2022-12-21 00:00:00
end: 2023-12-27 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © DojiEmoji (kevinhhl)

//@version=4
strategy("[KL] Relative Volume + ATR Strategy",overlay=true,pyramiding=1)
ENUM_LONG = "Long"

// Timeframe {
backtest_timeframe_start = input(defval = timestamp("01 Apr 2016 13:30 +0000"), title = "Backtest Start Time", type = input.time)
USE_ENDTIME = input(false,title="Define backtest end-time (If false, will test up to most recent candle)")
backtest_timeframe_end = input(defval = timestamp("01 May 2021 19:30 +0000"), title = "Backtest End Time (if checked above)", type = input.time)
within_timeframe = true
// }
len_volat = input(14,title="Length of ATR to determine volatility")
ATR_volat = atr(len_volat)
avg_ATR_volat  = sma(ATR_volat, len_volat)
std_ATR_volat = stdev(ATR_volat, len_volat)
// }

// Trailing stop loss {
ATR_X2_TSL = atr(input(14,title="Length of ATR for trailing stop loss")) * input(2.0,title="ATR Multiplier for trailing stop loss",type=input.float)
TSL_source = low
var stop_loss_price = float(0)
TSL_line_color = color.green, TSL_transp = 100
if strategy.position_size == 0 or not within_timeframe
    TSL_line_color := color.black
    stop_loss_price := TSL_source - ATR_X2_TSL 
else if strategy.position_size > 0
    stop_loss_price := max(stop_loss_price, TSL_source - ATR_X2_TSL)
    TSL_transp := 0
plot(stop_loss_price, color=color.new(TSL_line_color, TSL_transp))
// }

// Signals for entry {
_avg_vol = sma(volume,input(20, title="SMA(volume) length (for relative comparison)"))
_relative_vol = _avg_vol * input(1.5,title="Multiple of avg vol to consider relative volume as being high",type=input.float)
__lowerOfOpenClose = min(open,close)
_wickRatio_lower = (__lowerOfOpenClose - low) / (high - low)
entry_signal1 = volume > _relative_vol
entry_signal2 = ATR_volat < avg_ATR_volat + std_ATR_volat and ATR_volat > avg_ATR_volat - std_ATR_volat
// }


alert_per_bar(msg)=>
    prefix = "[" + syminfo.root + "] "
    suffix = "(P=" + tostring(close) + "; atr=" + tostring(ATR_volat) + ")"
    alert(tostring(prefix) + tostring(msg) + tostring(suffix), alert.freq_once_per_bar)

// MAIN:
if within_timeframe
    if strategy.position_size > 0 and strategy.position_size[1] > 0 and (stop_loss_price/stop_loss_price[1]-1) > 0.005
        alert_per_bar("TSL raised to " + tostring(stop_loss_price))

    // EXIT ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::    // placed before entry, will re-enter if stopped out
	exit_msg = close <= strategy.position_avg_price ? "stop loss" : "take profit"
	if strategy.position_size > 0 and TSL_source <= stop_loss_price
        strategy.close(ENUM_LONG, comment=exit_msg)

    // ENTRY :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
	if entry_signal1 and entry_signal2// and entry_signal3
		entry_msg = strategy.position_size > 0 ? "adding" : "initial"
		strategy.entry(ENUM_LONG, strategy.long, comment=entry_msg)

// CLEAN UP:
if strategy.position_size == 0
	stop_loss_price := float(0)

Detail

https://www.fmz.com/strategy/436906

Last Modified

2023-12-28 17:54:44