gavindiaz/strategies
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
b58acabe89d2d4b3390d20bcda0a9fa9b2486196
strategies/追踪趋势获利策略Take-Profit-on-Trend-Strategy.md
T
root 286d50b04c update
2023-10-31 12:29:56 +08:00

9.7 KiB
Raw Blame History

Name

追踪趋势获利策略Take-Profit-on-Trend-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

追踪趋势获利策略旨在检测资产的长期趋势和短期回调,在长期看涨的同时抓住短期调整的机会建仓,并设置合理的止盈止损线,以便顺势而为,及时止盈止损。

策略原理

该策略主要基于EMA均线和RSI指标判断长短期趋势。具体来说,它使用50日线EMA和200日线EMA判断长期趋势,使用RSI指标判断长期趋势强弱。当长期处于上涨趋势(200日线上涨)且强势(RSI大于50)的同时,短期出现回调(最近2根K线收盘价下跌)时,做多入场。

在入场后,策略设置了止盈止损条件。当价格较入场价上涨超过2倍BHD单位时,做多盈利了结;当价格较入场价下跌超过3倍BHD单位时,止损平仓。其中,BHD单位是根据最近200根K线的振幅计算得到。

这样,该策略充分考虑了长短期趋势特征,在增强盈利的同时控制风险,既可顺势而为,又可及时止盈止损。

优势分析

该策略具有以下优势:

  1. 考虑长短期趋势特征,同时结合强弱指标,避免在震荡市场盲目建仓。

  2. 追踪趋势而建仓,顺应市场方向,胜率较高。

  3. 设置止盈止损点,有利于及时获利了结和控制风险。

  4. 止盈止损点根据市场波动性计算,可动态调整,较为合理。

  5. 回测数据表明,在多种币对和周期上,该策略收益较高,稳定性良好。

  6. 策略思路简单清晰,易于理解和实现,适合不同水平的交易者。

风险分析

该策略也存在一定风险:

  1. 长短期判断可能存在错误,建仓方向误判的可能。

  2. 市场可能出现断崖式下跌,止损点无法完全避免巨额亏损的风险。

  3. 参数设置(如均线周期等)不当可能影响策略效果。

  4. 止盈点设置过小,可能过早离场影响收益。

  5. 回测数据不代表实盘表现,实盘期间需持续优化。

对应风险的解决方法:

  1. 优化参数,调整均线周期,或加入其他指标判断强弱。

  2. 可设置止损幅度较大,或加入降低仓位等风控机制。

  3. 多做回测,评估不同参数对策略的影响。

  4. 动态优化止盈参数,根据市场情况调整止盈幅度。

  5. 持续回测与优化,结合实盘进行调整,使策略更稳定。

优化方向

该策略可以从以下几个方面进行进一步优化:

  1. 优化参数设置,如调整均线周期、BHD单位周期等,找出最优参数组合。

  2. 增加其他指标判断,如MACD、KD等,使短期判断更准确。

  3. 优化止盈止损策略,如根据波动率动态调整止盈幅度等。

  4. 添加仓位管理策略,如趋势强度影响仓位大小等。

  5. 测试更多品种和周期的数据,评估策略健壮性。

  6. 添加过滤条件,例如收盘价高于开盘价等,可避免陷阱。

  7. 增加机器学习等先进技术,使策略更自动化、智能化。

通过以上优化,可以提高策略的胜率、收益率、稳定性、适应性等方面表现。

总结

追踪趋势获利策略整体来看,具有考虑长短期特征、顺势而为、止盈止损明确等优点,是一种较为稳定高效的趋势跟踪策略。但也存在一定风险,需要对参数及规则进行持续优化测试,结合实盘情况调整。整体来说,该策略思路清晰易操作,值得交易者学习借鉴。如果进一步优化,可以成为稳定可靠的量化交易策略之一。

||

Overview

The Take Profit on Trend strategy aims to detect long-term trends and short-term pullbacks, taking long positions during overall uptrends while capturing short-term dips, with reasonable stop loss and take profit levels set to follow the trend and take profits in a timely manner.

Strategy Logic

The strategy mainly uses EMA and RSI to determine long-term and short-term trends. Specifically, it uses 50-day EMA and 200-day EMA to judge long-term trends, and RSI to gauge trend strength. When the long-term is in an uptrend (200-day EMA rising) and strong (RSI above 50), and the short-term sees a pullback (last 2 candlesticks closing lower), a long position is taken.

After entering a position, the strategy sets stop loss and take profit conditions. When the price rises more than 2x BHD units above the entry price, profits are taken. When the price falls more than 3x BHD units below the entry price, the position is stopped out. The BHD unit is calculated based on the amplitude of the last 200 candlesticks.

This way, the strategy fully considers long and short term trend characteristics, increasing profits while controlling risks, following the trend while taking timely profits.

Advantage Analysis

The strategy has the following advantages:

  1. Considers long and short term trends, combined with strength indicators, avoids blind entries in ranging markets.

  2. Entries follow the trend direction, higher win rate.

  3. Take profit and stop loss points allow timely profit taking and risk control.

  4. TP and SL are dynamic based on volatility, relatively reasonable.

  5. Backtests show good returns and stability across symbols and timeframes.

  6. Simple and clear logic, easy to understand and implement for all skill levels.

Risk Analysis

The strategy also has some risks:

  1. Long/short term misjudgement leading to wrong entry directions.

  2. Cliff-like market crashes may penetrate stops.

  3. Poor parameter settings negatively impact performance.

  4. TP set too tight, may exit prematurely.

  5. Backtest ≠ live performance, continuous optimization needed.

Solutions:

  1. Optimize parameters, adjust MA periods, add cross-validation indicators.

  2. Wider stops, position sizing, other risk controls.

  3. Extensive backtesting to evaluate parameters.

  4. Dynamic TP optimization based on market conditions.

  5. Ongoing backtesting, optimization, live adjustment.

Optimization Directions

The strategy can be further optimized by:

  1. Parameter tuning, MA periods, BHD unit periods etc.

  2. Adding indicators, MACD, KD etc for better short-term accuracy.

  3. Optimizing TP/SL, dynamic size based on volatility etc.

  4. Adding position sizing based on trend strength.

  5. Testing robustness across more symbols and timeframes.

  6. Adding filters like closing price > open to avoid traps.

  7. Incorporating machine learning for more automation and intelligence.

These can improve win rate, return, stability, adaptiveness etc.

Conclusion

Overall the Take Profit on Trend strategy has the advantages of considering long/short trends, following trends, clear TP/SL. It is a stable and efficient trend following approach. But risks exist, requiring ongoing optimization and live adjustment. The logic is clear and easy to implement. Worth studying and applying for traders. With further optimization it can become a robust quant strategy.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 2021 Start year
v_input_2 true Start month
v_input_3 true Start day
v_input_4 2050 end year
v_input_5 true end month
v_input_6 true end day

Source (PineScript)

/*backtest
start: 2023-08-26 00:00:00
end: 2023-09-25 00:00:00
period: 1h
basePeriod: 15m
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/
// © BHD_Trade_Bot

// @version=5
strategy(
 shorttitle            = 'Take Profit On Trend',
 title                 = 'Take Profit On Trend (by BHD_Trade_Bot)',
 overlay               = true,
 calc_on_every_tick    = true,
 calc_on_order_fills   = true,
 use_bar_magnifier     = true,
 initial_capital       = 1000,
 default_qty_type      = strategy.percent_of_equity,
 default_qty_value     = 100,
 commission_type       = strategy.commission.percent,
 commission_value      = 0.1)



// Backtest Time Period
start_year   = input(title='Start year'   ,defval=2021)
start_month  = input(title='Start month'  ,defval=1)
start_day    = input(title='Start day'    ,defval=1)
start_time = timestamp(start_year, start_month, start_day, 00, 00)

end_year     = input(title='end year'     ,defval=2050)
end_month    = input(title='end month'    ,defval=1)
end_day      = input(title='end day'      ,defval=1)
end_time = timestamp(end_year, end_month, end_day, 23, 59)

is_back_test_time() => true



// EMA
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)

// RSI
rsi200 = ta.rsi(close, 200)

// EMA_CD
emacd = ema50 - ema200
emacd_signal = ta.ema(emacd, 50)
hist = emacd - emacd_signal

// BHD Unit
bhd_unit = ta.rma(high - low, 200) * 2
bhd_upper = ema200 + bhd_unit
bhd_lower = ema200 - bhd_unit



// All n candles is going down
all_body_decrease(n) =>
    isValid = true
    for i = 0 to (n - 1)
        if (close[i] > close[i + 1])
            isValid := false
            break
    isValid



// ENTRY CONDITIONS

// Long-term uptrend
entry_condition1 = rsi200 > 51 and hist > 0

// Short-term downtrend
entry_condition2 = all_body_decrease(2)

ENTRY_CONDITIONS = entry_condition1 and entry_condition2

if ENTRY_CONDITIONS and is_back_test_time()
    strategy.entry('entry', strategy.long)


// CLOSE CONDITIONS

// Price increase 2 BHD unit
take_profit = close > strategy.position_avg_price + bhd_unit * 2

// Price decrease 3 BHD unit
stop_loss = close < strategy.position_avg_price - bhd_unit * 3

CLOSE_CONDITIONS = take_profit or stop_loss

if CLOSE_CONDITIONS
    strategy.close('entry')



// Draw
plot(ema50, color=color.orange, linewidth=2)
plot(ema200, color=color.purple, linewidth=2)
bhd_upper_line = plot(bhd_upper, color=color.teal)
bhd_lower_line = plot(bhd_lower, color=color.teal)
fill(bhd_upper_line, bhd_lower_line, color=color.new(color.teal, 90))

Detail

https://www.fmz.com/strategy/427860

Last Modified

2023-09-26 11:22:04

Reference in New Issue View Git Blame Copy Permalink