Piyush Ratnu XAUUSD Trading Algorithm

Piyush Ratnu XAUUSD Trading Algorithm | April 2026 Version

A practical Piyush Ratnu-style reverse-engineered framework for projecting Gold price zones.

It is a structured model built from the recurring elements associated with that style of gold analysis:
Murray Math + macro pressure + real yields + USD + energy shock + volatility expansion + liquidity zones.

piyush ratnu trading algorithm for xauusd april 2026 version

1. Core Philosophy

The model assumes that Gold is driven by five dominant forces:

Factor	Why it matters
Real yields	Gold weakens when real yields rise and strengthens when they fall
USD strength	Gold is inversely sensitive to broad dollar strength
Energy shock / oil	Oil-led inflation can support gold, but only if yields do not rise too fast
Risk premium / geopolitics	Adds safe-haven premium and volatility expansion
Market structure / Murray Math	Gives the likely liquidity zones for reaction, rejection, or breakout

So the algorithm is not one single line. It is a weighted score model.

2. PR Fair Value Engine

Step 1: Build the Macro Pressure Score

$(w_1 \cdot RY) + (w_2 \cdot USD) + (w_3 \cdot OIL) + (w_4 \cdot RISK) + (w_5 \cdot FED) + (w_6 \cdot VOL)$

Where:

RY = real yield pressure score
USD = dollar strength score
OIL = oil/inflation shock score
RISK = geopolitical stress score
FED = Fed dovish/hawkish score
VOL = volatility expansion score

A cleaner practical version:

$MPS = -0.30(RY_z) -0.25(DXY_z) +0.15(OIL_z) +0.10(RISK_z) +0.10(FED_z) +0.10(VOL_z)$

Interpretation

Negative scores = bearish for gold
Positive scores = bullish for gold
Near zero = range / mean reversion regime

3. Price Projection Formula

Let current gold price be $P_0$ .

Projected fair value shift:

$ΔP=ATRn⋅K⋅MPS\Delta P = ATR_n \cdot K \cdot MPS$

Then:

$Pfair=P0+ΔPP_{fair} = P_0 + \Delta P$

Where:

ATR_n = average true range of chosen timeframe
K = scaling constant, usually between 1.2 and 2.5 depending on regime
MPS = macro pressure score

Example:

Current price = 4700
ATR(14, H4) = 85
K = 1.8
MPS = +0.9

Then:

$ΔP=85×1.8×0.9=137.7\Delta P = 85 \times 1.8 \times 0.9 = 137.7$ $Pfair≈4700+138=4838P_{fair} \approx 4700 + 138 = 4838$

That gives the macro fair value target zone.

4. Murray Math Overlay

Now map the fair value into Murray zones.

Use these reaction bands:

Murray Zone	Meaning
0/8	Extreme oversold base
2/8	Reversal support
4/8	Major equilibrium
6/8	Pivot resistance
8/8	Major distribution ceiling
+1/8, +2/8	Blow-off extension

Rule:

If P_fair falls near 4/8 to 6/8, expect range trade
If P_fair rises into 8/8 or +1/8, expect exhaustion or breakout test
If P_fair drops into 2/8 or 0/8, expect panic flush then absorption

5. PR Zone Construction Formula

Instead of a single target, create a zone:

$P_{fair} + (0.5 \cdot ATR)$ $P_{fair} – (0.5 \cdot ATR)$

So if fair value is 4838 and ATR is 85:

Upper zone = 4880
Lower zone = 4796

Projected zone:

4795–4880

That is much more realistic than a single exact print.

6. Probability Engine

Now score direction probability.

Bullish Probability

$P_{bull} = 50 + 12(RISK_z) + 15(-RY_z) + 10(-DXY_z) + 8(FED_z) + 5(MOM_z)$

Bearish Probability

$P_{bear} = 50 + 15(RY_z) + 12(DXY_z) – 8(FED_z) – 7(RISK_z) – 5(MOM_z)$

Then normalize to 100%.

In plain English:

Falling real yields = big bullish weight
Rising DXY = big bearish weight
Geopolitical stress = bullish weight
Hawkish Fed = bearish weight
Positive momentum = adds confirmation

7. Regime Filter

The PR-style framework works best if you first decide the regime.

Regime classification:

$\begin{cases} Trend, & \text{if ADX > 25 and ATR rising} \\ Range, & \text{if ADX < 20 and ATR flat} \\ Shock, & \text{if Oil z-score > 1.5 or Risk z-score > 1.5} \end{cases}$

Application

Trend regime: trade breakouts at Murray extremes
Range regime: fade 6/8 and buy 2/8–4/8
Shock regime: widen zones, reduce size, expect overshoots

8. Full Practical PR Formula

A usable combined model:

$PR\ Score = -0.30(RY_z) -0.25(DXY_z) +0.15(OIL_z) +0.10(RISK_z) +0.10(FED_z) +0.10(VOL_z) +0.10(MOM_z)$ $ScoreProjected\ Move = ATR \times 1.8 \times PR\ Score$ $MoveTarget\ Fair\ Value = Current\ Price + Projected\ Move$ $Value±0.5(ATR)Target\ Zone = Fair\ Value \pm 0.5(ATR)$

Then validate against:

Murray 4/8, 6/8, 8/8
Previous week high/low
Real yield direction
DXY trend
Oil breakout condition

9. Simplified Trading Rules

Condition	Action
PR Score > +0.75 and price above 4/8	Buy dips
PR Score > +1.25 and breaks 8/8	Buy breakout
PR Score between -0.40 and +0.40	Range trade only
PR Score < -0.75 and price below 6/8	Sell rallies
PR Score < -1.25 with rising yields + DXY	Sell breakdown

10. Example Setup

Assume:

XAUUSD = 4705
ATR(H4) = 90
Real yields rising moderately = +0.8
DXY firm = +0.7
Oil elevated = +0.5
Geopolitical risk = +0.6
Fed slightly dovish = +0.3
Momentum neutral = 0

Then:

$Score=−0.30(0.8)−0.25(0.7)+0.15(0.5)+0.10(0.6)+0.10(0.3)+0.10(0)PR\ Score = -0.30(0.8)-0.25(0.7)+0.15(0.5)+0.10(0.6)+0.10(0.3)+0.10(0)$ $Score=−0.24−0.175+0.075+0.06+0.03=−0.25PR\ Score = -0.24-0.175+0.075+0.06+0.03 = -0.25$

Projected move:

$90 \times 1.8 \times (-0.25)= -40.5$

Fair value:

$4705 - 41 = 4664$

Zone:

$4664 \pm 45$

So projected trade zone:

4619–4709

Interpretation:

Macro not bearish enough for collapse
Bias mildly negative
Sell upper band, buy panic flushes near deeper support

11. Excel Version

You can build this in Excel or Google Sheets as:

=CurrentPrice + (ATR*1.8*((-0.30*RY_Z)+(-0.25*DXY_Z)+(0.15*OIL_Z)+(0.10*RISK_Z)+(0.10*FED_Z)+(0.10*VOL_Z)+(0.10*MOM_Z)))

Upper band:

=FairValue + (0.5*ATR)

Lower band:

=FairValue – (0.5*ATR)

12. Python Logic Skeleton

def pr_score(ry, dxy, oil, risk, fed, vol, mom):
return (–0.30 * ry) + (–0.25 * dxy) + (0.15 * oil) + (0.10 * risk) + (0.10 * fed) + (0.10 * vol) + (0.10 * mom)

def projected_zone(current_price, atr, score, k=1.8):
fair_value = current_price + (atr * k * score)
upper = fair_value + (0.5 * atr)
lower = fair_value – (0.5 * atr)
return {
“score”: round(score, 3),
“fair_value”: round(fair_value, 2),
“zone_low”: round(lower, 2),
“zone_high”: round(upper, 2)
}

score = pr_score(
ry=0.8,
dxy=0.7,
oil=0.5,
risk=0.6,
fed=0.3,
vol=0.0,
mom=0.0
)

print(projected_zone(4705, 90, score))

13. Best Use of This Algorithm

This model is strongest for:

next 1 to 5 trading days
macro-event windows
post-FOMC / CPI / NFP / war headline repricing
identifying zone targets, not exact ticks

This model is weaker for:

ultra-fast scalping
very quiet sessions
price manipulation spikes without macro confirmation

14. PR-Style Conclusion

The core idea is simple:

Gold target projection = macro pressure score translated into ATR-based price displacement, then anchored to Murray Math liquidity zones.

So the real engine is:

Measure macro pressure
Translate it into projected move
Map it onto structural liquidity zones
Trade probabilities, not predictions