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双重指标反转买点策略Dual-Indicators-Bottom-Buying-Strategy.md

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Name

双重指标反转买点策略Dual-Indicators-Bottom-Buying-Strategy

Author

ChaoZhang

Strategy Description

IMG [trans]

概述

该策略通过结合交易量和RSI指标来确定买入时机,采用分批设置止盈目标逐步获利的方式管理头寸。该策略适用于震荡行情,可以有效锁定小幅度涨落震荡中重复出现的买点。

策略原理

该策略使用两个指标来识别买入时机:交易量和RSI。具体逻辑是,当交易量超过最近70天交易量平均值的2.5倍,同时RSI低于30(超卖水平)时,发出买入信号。

一旦买入头寸建立,该策略会设置5个不同的止盈目标(0.4%、0.6%、0.8%、1.0%和1.2%),并按照头寸比例(20%、40%、60%、80%和100%)逐步止盈,直到全部头寸平仓。同时设置5%的止损点。

这样,通过分批设置止盈可以锁定小幅度涨势,避免因等待更大涨幅而错失利润。止损可以控制单笔损失。

优势分析

该策略具有以下优势:

  1. 使用双重指标识别买点,避免假突破。交易量放大可以确认底部承压力度,RSI超卖可以判断Addon反弹概率。

  2. 采用分批止盈策略,可以最大限度锁定小幅震荡中的获利机会,无需等待大幅上涨也可以获利。

  3. 适用于震荡行情,特别是价格在机构unfinished区域反复横跳的市场。这类市场短期难有确定方向,该策略可以频繁获利。

  4. 停损点设置得较宽,给予市场足够的决策空间。避免因短期价格调整被五未平仓。

风险分析

该策略的主要风险有:

  1. 双重指标确认信号存在误判风险,可能买入假突破点位。可通过参数优化降低风险。

  2. 分批止盈可能因持仓量过小错失大涨机会。可通过调整止盈点位和头寸比例优化。

  3. 停损幅度较大,单笔亏损可能较大。可降低仓位数管理风险。

  4. 适合震荡市场,强势市存在较大方向性风险。需关注大级别市场结构。

  5. 较高交易频率导致交易成本增加。优选低手续费交易平台。

优化方向

该策略可以从以下方面进行优化:

  1. 优化交易量和RSI参数组合,降低误判率。还可以引入MACD,KDJ等其它指标进行确认。

  2. 测试不同止盈幅度和头寸比例,找到最优参数组合。还可以引入动态止盈机制。

  3. 优化仓位管理策略,通过风险仓位管理系统减小单笔亏损的概率。

  4. 增加趋势判断模块,能够识别趋势转向并及时止损。避免持仓过于passive。

  5. 引入算法交易和量化回测系统,快速遍历不同参数,寻找最优参数组合。

  6. 借鉴机构级high频交易策略的滑点控制和成本控制模型,降低交易次数同时保证盈利率。

总结

该双重指标反转买点策略,通过交易量放量加RSI超卖判断底部,采用分批息盈的方法锁定震荡行情中的小幅获利。优点是频繁获利,无需等待大幅行情;缺点是容易误判信号和高交易频率。可通过多指标优化确认信号质量,通过风险控制和成本控制提高策略稳定性。该策略适合短期locking小幅度利润。

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Overview

This strategy identifies buying opportunities by combining trading volume and RSI indicators. It manages positions using staged take profit targets to lock in gains gradually. The strategy works well during range-bound markets and can effectively capture recurring buying signals within small price swings.

Strategy Logic

The strategy utilizes two indicators to identify buy signals - trading volume and RSI. Specifically, it goes long when volume surpasses 2.5 times the 70-day average volume, together with RSI dropping below 30 (oversold levels).

Once a long position is established, the strategy sets 5 take profit targets at 0.4%, 0.6%, 0.8%, 1.0% and 1.2%. It closes out positions gradually based on position ratio (20%, 40%, 60%, 80% and 100%) until fully exiting. A 5% stop loss is also set.

By taking profits in stages, it aims to lock in gains amid minor upside moves, instead of waiting for larger runs which may not materialize. The stop loss controls downside risk on a per trade basis.

Advantage Analysis

The key advantages of this strategy are:

  1. Using dual indicators prevents false breakouts. Elevated volume confirms bottom conviction while oversold RSI signals mean reversion odds.

  2. Taking profits in batches allows maximizing small upside captures within ranges. No need to wait for huge runs to make money.

  3. Excels in range-bound markets, especially those stuck around institutional unfinished areas. Frequent small gains can be captured in the absence of trend.

  4. Wide stop loss allows markets room for whipsaws before getting stopped out. Avoids prematurely exiting short-term retracements.

Risk Analysis

The main risks are:

  1. Dual signal misinterpretation leading to false entries. Can be mitigated via parameter optimization.

  2. Staged profit taking risks missing large trend moves due to small position sizing. Optimizing take profit levels and position ratios helps.

  3. Wide stops lead to potentially large single-trade losses. Position sizing is key to managing risk.

  4. Strong trending markets pose directional bias risks. Pay attention to larger timeframe structure.

  5. High trade frequency increases transaction costs. Using low-commission brokers is preferred.

Optimization Directions

Possible optimization directions include:

  1. Optimizing volume and RSI combinations to reduce false signals. Adding confirmations like MACD and KDJ.

  2. Testing different take profit levels and position ratios for ideal configurations, potentially with dynamic mechanisms.

  3. Introducing position sizing rules to cut max risk per trade via risk management systems.

  4. Incorporating trend metrics to detect reversals for timely stop losses. Avoid overholding in shifting markets.

  5. Leveraging algorithmic backtesting to swiftly iterate parameters for best configurations.

  6. Learning from institutional HFT slippage/cost control models to improve efficiency despite high turnover.

Conclusion

This dual indicator mean reversion strategy identifies bottom signals with volume surges and oversold RSI for buying, taking gradual profits amid ranges via staged exits. It profits frequently without requiring huge runs. Downsides include signal misinterpretation risks and high turnover. Confirmation optimization and risk/cost controls improve robustness. Excellent for short-term gains harvesting in choppy markets.

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Strategy Arguments

Argument Default Description
v_input_int_1 70 Volume lenght
v_input_int_2 20 RSI lenght
v_input_float_1 0.4   TP 1
v_input_float_2 0.6   TP 2
v_input_float_3 0.8   TP 3
v_input_float_4 true   TP 4
v_input_float_5 1.2   TP 5
v_input_int_3 20   % TP 1 Q
v_input_int_4 40   % TP 2 Q
v_input_int_5 60   % TP 3 Q
v_input_int_6 80   % TP 4 Q
v_input_int_7 100   % TP 5 Q
v_input_float_6 5 % Stop Loss

Source (PineScript)

/*backtest
start: 2023-12-27 00:00:00
end: 2024-01-03 00:00:00
period: 1m
basePeriod: 1m
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/
// © wielkieef

//@version=5

strategy(title='BTFD strategy [3min]', overlay=true, pyramiding=5, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, calc_on_order_fills=false, slippage=0, commission_type=strategy.commission.percent, commission_value=0.03)


// Volume

vol_sma_length = input.int(70, title='Volume lenght  ', minval=1)
Volume_condt = volume > ta.sma(volume, vol_sma_length) * 2.5


// Rsi

rsi_lenght = input.int(20, title='RSI lenght', minval=0)
rsi_up = ta.rma(math.max(ta.change(close), 0), rsi_lenght)
rsi_down = ta.rma(-math.min(ta.change(close), 0), rsi_lenght)
rsi_value = rsi_down == 0 ? 100 : rsi_up == 0 ? 0 : 100 - 100 / (1 + rsi_up / rsi_down)

rsi_overs = rsi_value <= 30
rsi_overb = rsi_value >= 70


// logic

tp_1 = input.float(0.4,"  TP 1", minval=0.1, step=0.1)
tp_2 = input.float(0.6,"  TP 2", minval=0.2, step=0.1)
tp_3 = input.float(0.8,"  TP 3", minval=0.3, step=0.1)
tp_4 = input.float(1.0,"  TP 4", minval=0.4, step=0.1)
tp_5 = input.float(1.2,"  TP 5", minval=0.5, step=0.1)

q_1 = input.int(title='  % TP 1 Q ', defval=20,  minval=1, step=10)
q_2 = input.int(title='  % TP 2 Q ', defval=40,  minval=1, step=10)
q_3 = input.int(title='  % TP 3 Q ', defval=60,  minval=1, step=10)
q_4 = input.int(title='  % TP 4 Q ', defval=80,  minval=1, step=10)
q_5 = input.int(title='  % TP 5 Q ', defval=100, minval=1, step=10)

sl = input.float(5.0, '% Stop Loss', step=0.1)

long_cond = Volume_condt and rsi_overs

// this code is from author RafaelZioni, modified by wielkieef
per(procent) =>
    strategy.position_size != 0 ? math.round(procent / 100 * strategy.position_avg_price / syminfo.mintick) : float(na)
// --------------------------------------------------------------------------------------------------------------------

if  long_cond
    strategy.entry('BUY', strategy.long)

strategy.exit('TP 1', qty_percent=q_1, profit=per(tp_1), loss=per(sl) )
strategy.exit('TP 2', qty_percent=q_2, profit=per(tp_2), loss=per(sl) )
strategy.exit('TP 3', qty_percent=q_3, profit=per(tp_3), loss=per(sl) )
strategy.exit('TP 4', qty_percent=q_4, profit=per(tp_4), loss=per(sl) )
strategy.exit('TP 5', qty_percent=q_5, profit=per(tp_5), loss=per(sl) )

 
// by wielkieef

Detail

https://www.fmz.com/strategy/437683

Last Modified

2024-01-04 17:39:13