基于资金曲线的仓位管理策略


创建日期: 2024-01-16 15:06:39 最后修改: 2024-01-16 15:06:39
复制: 1 点击次数: 738
avatar of ChaoZhang ChaoZhang
1
关注
1617
关注者

基于资金曲线的仓位管理策略

策略概述

本策略的核心思路是根据资金曲线的走势来动态调整仓位大小,在盈利时加大仓位,在亏损时减小仓位,以控制整体风险。该策略同时结合了Chande动量指标、SuperTrend指标和动量指标来识别交易信号。

策略原理

该策略使用两种方式判断资金曲线是否处于下行趋势:1)计算资金曲线的快速和慢速简单移动平均线,如果快速SMA低于慢速SMA,则判断为下行;2)计算资金曲线与其本身较长周期的简单移动平均线,如果资金曲线低于该移动平均线,则判断为下行。

当判断资金曲线下行时,仓位会根据设定减少或增加一定比例。例如,如果设定减少50%,则原先10%的仓位会减少至5%。该策略使得盈利时加大仓位规模,亏损时减小仓位规模,以控制整体风险。

策略优势

  • 利用资金曲线判断系统整体盈亏情况,动态调整仓位有助于控制风险
  • 结合多个指标判断入场,可提高获利概率
  • 可自定义仓位调整的参数,适应不同风险偏好

策略风险

  • 仓位放大时亏损也会被放大
  • 参数设置不当可能导致仓位调整过于激进
  • 仓位管理并不能完全规避系统性风险

优化思路

  • 测试不同仓位调整参数的效果
  • 尝试其他指标组合判断资金曲线走势
  • 优化入场条件,提高胜率

总结

本策略总体思路清晰,利用资金曲线动态调整仓位,可有效控制风险,值得进一步测试与优化。参数设置和止损策略也需要充分考虑,避免激进操作带来的风险。

策略源码
/*backtest
start: 2024-01-08 00:00:00
end: 2024-01-15 00:00:00
period: 3m
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/
// © shardison
//@version=5

//EXPLANATION
//"Trading the equity curve" as a risk management method is the 
//process of acting on trade signals depending on whether a system’s performance
//is indicating the strategy is in a profitable or losing phase.
//The point of managing equity curve is to minimize risk in trading when the equity curve is  in a downtrend. 
//This strategy has two modes to determine the equity curve downtrend:
//By creating two simple moving averages of a portfolio's equity curve - a short-term
//and a longer-term one - and acting on their crossings. If the fast SMA is below
//the slow SMA, equity downtrend is detected (smafastequity < smaslowequity).
//The second method is by using the crossings of equity itself with the longer-period SMA (equity < smasloweequity).
//When "Reduce size by %" is active, the position size will be reduced by a specified percentage
//if the equity is "under water" according to a selected rule. If you're a risk seeker, select "Increase size by %"
//- for some robust systems, it could help overcome their small drawdowns quicker.

strategy("Use Trading the Equity Curve Postion Sizing", shorttitle="TEC", default_qty_type = strategy.percent_of_equity, default_qty_value = 10, initial_capital = 100000)

//TRADING THE EQUITY CURVE INPUTS
useTEC           = input.bool(true, title="Use Trading the Equity Curve Position Sizing")
defulttraderule  = useTEC ? false: true
initialsize      = input.float(defval=10.0, title="Initial % Equity")
slowequitylength = input.int(25, title="Slow SMA Period")
fastequitylength = input.int(9, title="Fast SMA Period")
seedequity = 100000 * .10
if strategy.equity == 0
    seedequity
else
    strategy.equity
slowequityseed   = strategy.equity > seedequity ? strategy.equity : seedequity
fastequityseed   = strategy.equity > seedequity ? strategy.equity : seedequity
smaslowequity    = ta.sma(slowequityseed, slowequitylength)
smafastequity    = ta.sma(fastequityseed, fastequitylength)
equitycalc       = input.bool(true, title="Use Fast/Slow Avg", tooltip="Fast Equity Avg is below Slow---otherwise if unchecked uses Slow Equity Avg below Equity")
sizeadjstring    = input.string("Reduce size by (%)", title="Position Size Adjustment", options=["Reduce size by (%)","Increase size by (%)"])
sizeadjint       = input.int(50, title="Increase/Decrease % Equity by:")
equitydowntrendavgs = smafastequity < smaslowequity
slowequitylessequity = strategy.equity < smaslowequity

equitymethod = equitycalc ? equitydowntrendavgs : slowequitylessequity

if sizeadjstring == ("Reduce size by (%)")
    sizeadjdown = initialsize * (1 - (sizeadjint/100))
else
    sizeadjup = initialsize * (1 + (sizeadjint/100))
c = close
qty = 100000 * (initialsize / 100) / c
if useTEC and equitymethod
    if sizeadjstring == "Reduce size by (%)"
        qty := (strategy.equity * (initialsize / 100) * (1 - (sizeadjint/100))) / c
    else
        qty := (strategy.equity * (initialsize / 100) * (1 + (sizeadjint/100))) / c
    
//EXAMPLE TRADING STRATEGY INPUTS
CMO_Length = input.int(defval=9, minval=1, title='Chande Momentum Length')
CMO_Signal = input.int(defval=10, minval=1, title='Chande Momentum Signal')

chandeMO = ta.cmo(close, CMO_Length)
cmosignal = ta.sma(chandeMO, CMO_Signal)

SuperTrend_atrPeriod = input.int(10, "SuperTrend ATR Length")
SuperTrend_Factor = input.float(3.0, "SuperTrend Factor", step = 0.01)
Momentum_Length = input.int(12, "Momentum Length")
price = close

mom0 = ta.mom(price, Momentum_Length)
mom1 = ta.mom( mom0, 1)
[supertrend, direction] = ta.supertrend(SuperTrend_Factor, SuperTrend_atrPeriod)
stupind = (direction < 0 ? supertrend : na)
stdownind = (direction < 0? na : supertrend)

//TRADING CONDITIONS
longConditiondefault = ta.crossover(chandeMO, cmosignal) and (mom0 > 0 and mom1 > 0 and close > stupind) and defulttraderule
if (longConditiondefault)
    strategy.entry("DefLong", strategy.long, qty=qty)

shortConditiondefault = ta.crossunder(chandeMO, cmosignal) and (mom0 < 0 and mom1 < 0 and close < stdownind) and defulttraderule
if (shortConditiondefault)
    strategy.entry("DefShort", strategy.short, qty=qty)
    
longCondition = ta.crossover(chandeMO, cmosignal) and (mom0 > 0 and mom1 > 0 and close > stupind) and useTEC
if (longCondition)
    strategy.entry("AdjLong", strategy.long, qty = qty)

shortCondition = ta.crossunder(chandeMO, cmosignal) and (mom0 < 0 and mom1 < 0 and close < stdownind) and useTEC
if (shortCondition)
    strategy.entry("AdjShort", strategy.short, qty = qty)
plot(strategy.equity)
plot(smaslowequity, color=color.new(color.red, 0))
plot(smafastequity, color=color.new(color.green, 0))