forked from animatedread/Warrior_EA
e9c562b39f
- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling. - Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations. - Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes. - Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing. - Implemented `read_book.py` for analyzing trade book data and correlations. - Developed `volnorm.py` for testing tick-volume normalization with new and old methods.
73 lines
2.8 KiB
MQL5
73 lines
2.8 KiB
MQL5
//+------------------------------------------------------------------+
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//| ModalityPrice.mqh |
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//| AnimateDread |
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//| |
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//| PRICE SHAPE, REGIME AND VOLATILITY of the signal bar - the things |
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//| that need nothing but the bar cache. Every distance is in ATR so |
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//| one number means the same on an index at 5,000 and one at 40,000; |
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//| ratios of two prices in the same units, never a raw price. |
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//+------------------------------------------------------------------+
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#ifndef WARRIOR_MODALITYPRICE_MQH
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#define WARRIOR_MODALITYPRICE_MQH
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#include "Modality.mqh"
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#include "..\System\BarCache.mqh"
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#include "..\System\RegimeMath.mqh"
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class CModalityPrice : public CModality
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{
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protected:
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CBarCache *m_cache; // not owned
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int m_volWarm; // bars before the volatility percentile may answer
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public:
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CModalityPrice(CBarCache *cache, const int volWarm = 250)
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: m_cache(cache), m_volWarm(volWarm) {}
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virtual string Name(void) const override { return "price"; }
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virtual bool Read(const SBarRef &bar, SMarketContext &ctx) override;
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};
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//+------------------------------------------------------------------+
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bool CModalityPrice::Read(const SBarRef &bar, SMarketContext &ctx)
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{
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const int i = bar.idx;
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if(m_cache == NULL || i < 100 || i >= m_cache.Count())
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return false;
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const double atr = m_cache.Atr(i);
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if(atr <= 0.0)
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return false;
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const double c = m_cache.Close(i), h = m_cache.High(i), l = m_cache.Low(i);
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double mean = 0.0, sd = 0.0;
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if(m_cache.MeanStd(i, 20, mean, sd) && sd > 0.0)
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ctx.v[CTX_Z] = (c - mean) / sd;
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if(mean > 0.0)
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ctx.v[CTX_DIST_ATR] = (c - mean) / atr;
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ctx.v[CTX_RET1_ATR] = (c - m_cache.Close(i - 1)) / atr;
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ctx.v[CTX_DROP5_ATR] = (c - m_cache.Close(i - 5)) / atr;
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ctx.v[CTX_RANGE_ATR] = (h - l) / atr;
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ctx.v[CTX_CLOSE_LOC] = (h > l) ? (c - l) / (h - l) : 0.5;
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double x[];
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if(m_cache.ClosesBack(i, CRegimeMath::LabelBars(), x))
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{
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const double er = CRegimeMath::Efficiency(x, 20);
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const double vr = CRegimeMath::Variance(x, 60, 5);
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ctx.v[CTX_ER] = er;
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ctx.v[CTX_VR] = vr;
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ctx.v[CTX_REGIME] = CRegimeMath::Code(er, vr);
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}
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const double pct = m_cache.VolPercentile(i, m_volWarm);
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if(pct >= 0.0)
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ctx.v[CTX_VOL_PCT] = pct;
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double atrSum = 0.0;
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int atrN = 0;
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for(int k = i - 99; k <= i; k++)
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if(m_cache.Atr(k) > 0.0)
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{
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atrSum += m_cache.Atr(k);
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atrN++;
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}
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if(atrN > 50)
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ctx.v[CTX_ATR_RATIO] = atr / (atrSum / atrN);
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return true;
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}
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#endif // WARRIOR_MODALITYPRICE_MQH
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