TB阅读_人人宽客01

人人宽客策略学习

整体感觉变现一般,高收益通过策略组合的多品种实现

TB阅读_人人宽客02

人人宽客C11

周期:30m
买入:std短期长期作为一层滤网,倍率ATR作为上下界
卖出:收盘价小于下界卖出,且,收盘价低于 “持有最高价和买入价差”倍率(这个不太明白做什么的,而且这个卖出条件偏严格,有点像止盈)
止损:买入价倍率止损

TB阅读_人人宽客03

人人宽客C31

周期:没看懂在干么,金手指什么鬼

TB阅读_人人宽客04

人人宽客C21

周期:15M等
买入:cci>100,且c>c_ma
卖出:止盈,cci<0,c>买入价倍率
止损:c<买入价倍率

TB阅读_人人宽客05

人人宽客C41

周期:15M
思路:进场:高低点均值构建上下轨,突破就是进场点,同时也是反向进场点。出场:出场开盘即确定好止损;达到一定盈利以后,出半仓。
买入:已high的移动平均双倍周期代替HH,大于hh买入
加仓买入:c<hh 且c>买入价倍率
卖出:
止损:c<变异hh,变异ll的平均值

TB阅读_钢铁侠01

钢铁侠T01

源代码:QM_IntradayHLRange
周期:
买入:高于上界买
卖出:低于下界卖
止损:
特色:使用日线信息构造上下界,类似DualThrust,但是融合时用max(h-closelow,closehigh-low)

TB阅读_钢铁侠02

钢铁侠11

买入:l[1]>ll[1],l[2]>ll[2],h>hh[1] mavol[1]>mavol[2]
卖出:
止损:最高级回撤止损atr

TB阅读_钢铁侠源码

01,QM_IntradayHLRange

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Params
Numeric shares(1);
Numeric Params3(2);
Numeric Params4(3);
Numeric Params2(0.70);
Numeric Params1(1.30);
Numeric Params5(9.15);
Vars
Numeric i(0);
Numeric n(0);
Numeric n_buyRange(0);
Numeric n_SellRange(0);
Numeric m_upper(0);
Numeric m_lower(0);
Numeric m_minPoint(0);
Numeric var6(0);
NumericSeries n_barCnt(0);
NumericSeries n_dayHigh(0);
NumericSeries n_dayLow(0);
NumericSeries m_dayClose(0);
Numeric n_buyRange1(0);
Numeric n_iowest(0);
Numeric n_closeHigh(0);
Numeric n_closeLow(0);


Begin
If(CurrentBar == 0 || Date != Date[1])
{
n_barCnt = 1;
n_dayHigh = High;
n_dayLow = Low;
}Else
{
n_barCnt = n_barCnt + 1;
If(High > n_dayHigh)
n_dayHigh = High;
If(Low < n_dayLow)
n_dayLow = Low;
}
m_dayClose = Close;

For i = 1 to Params3
{
If(i == 1)
{
n = n_barCnt;
n_highest= n_dayHigh[n];
n_iowest = n_dayLow[n];
n_closeHigh = m_dayClose[n];
n_closeLow = m_dayClose[n];
}Else
{
n = n + n_barCnt[n];
}
If(n_dayHigh[n] > n_buyRange1) n_highest= n_dayHigh[n];
If(n_dayLow[n] < n_iowest) n_iowest = n_dayLow[n];
If(m_dayClose[n] > n_closeHigh) n_closeHigh = m_dayClose[n];
If(m_dayClose[n] < n_closeLow) n_closeLow = m_dayClose[n];
}

n_buyRange = Max(n_highest- n_closeLow, n_closeHigh - n_iowest);

For i = 1 to Params4
{
If(i == 1)
{
n = n_barCnt;
n_highest= n_dayHigh[n];
n_iowest = n_dayLow[n];
n_closeHigh = m_dayClose[n];
n_closeLow = m_dayClose[n];
}Else
{
n = n + n_barCnt[n];
}
If(n_dayHigh[n] > n_buyRange1) n_highest= n_dayHigh[n];
If(n_dayLow[n] < n_iowest) n_iowest = n_dayLow[n];
If(m_dayClose[n] > n_closeHigh) n_closeHigh = m_dayClose[n];
If(m_dayClose[n] < n_closeLow) n_closeLow = m_dayClose[n];
}

n_SellRange = Max(n_highest- n_closeLow, n_closeHigh - n_iowest);
m_upper = OpenD(0) + n_buyRange * Params1;
m_lower = OpenD(0) - n_SellRange * Params2;
m_minPoint = PriceScale * MinMove;

If(Time < Params5 / 100) Return;
If(MarketPosition != 1)
{
If(High >= m_upper)
{
Buy(shares, Max(Open, m_upper));
Return;
}
}
If(MarketPosition != -1)
{
If(Low <= m_lower)
{
SellShort(shares, Min(Open, m_lower));
}
}
End

botvs策略阅读

基于回归幅度的反转交易策略

地址:https://www.fmz.com/bbs-topic/1394
基于:趋势
描述:

vnpy策略阅读

寻找来源:
百度:vnpy常见策略学习
google:VNPY策略分享
1,视频 VNPY套利实战班
2,vnpy社区
新社区:https://www.vnpy.com/forum/

本以为应该很多人用vnpy,但网上搜貌似也没多少策略被分享,想多读一读现成策略充充电。发现还不打好找

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