ZYTX GKDDThe ZYTX High-Sell Low-Buy Indicator Strategy is a trend-following indicator that integrates multiple indicator resonances. It demonstrates the perfect performance of an automated trading robot, truly achieving the high-sell low-buy strategy in trading.
Trend Analizi
FS JIMENEZ)FS JIMENEZ is a tactical breakout-retest strategy optimized for volatile price action and disciplined entries. It features:
• Swing structure validation
• Smart cooldown and price spacing logic
• SL compression after 3 bars
• Dynamic TP targeting based on candle strength and ATR
• Optional trailing SL via buffer multiplier
Built for traders seeking precision and controlled exposure across volati
XRP 4H UTC Edge | metaduro.com🔍 Strategy Overview
The XRP 16H Strong strategy is a time-based intraday trading system built specifically for XRP on the 4-hour chart. It focuses on the 16:00–20:00 UTC session, targeting short-term price moves during this high-activity window.
✅ Entry Condition
📅 Time = 16:00 UTC → A long position is opened at the start of the 16:00–20:00 UTC candle. This time period often captures late-day momentum and volatility.
❌ Exit Condition
📅 Time = 20:00 UTC → The position is closed at the start of the 20:00–00:00 UTC candle, realizing gains or cutting risk quickly.
📊 No Indicators Used
This strategy is purely based on time-of-day logic — no indicators like RSI, MACD, or moving averages are used. It aims to isolate specific market behavior tied to certain hours.
⚙️ Settings
• Timeframe: 4H (required for correct execution)
• Trade Type: Long only
• Position Size: 100% equity per trade (default setting, adjustable)
💡 Use Cases
• Ideal for testing statistically favorable trading hours
• Can be applied to other crypto assets or combined with filters (e.g., volume spikes, price action patterns)
• Simple foundation for more complex hybrid models (time + indicator-based)
📉 No Stop-Loss or Take-Profit Logic
This script does not use SL/TP. It's recommended to combine with external risk management or modify the code for tighter control.
🔁 EMA 3/21 Crossover Strategy — Exit on Opposite SignalEMA 3/21 Crossover Strategy — Exit on Opposite Signal
This strategy enters trades based on a crossover between two exponential moving averages:
Buy Entry: When the 3-period EMA crosses above the 21-period EMA
Sell Entry: When the 3-period EMA crosses below the 21-period EMA
Exit Rule: Positions are exited only when an opposite signal occurs (i.e., a new crossover in the other direction)
Key Features:
Designed for trend-following setups
Uses ATR-based SL/TP lines for visual reference only (trades do not auto-close at SL/TP)
Suitable for manual or automated trading logic with high trade clarity
Can be applied on any timeframe and any liquid instrument (Forex, crypto, indices, etc.)
Recommended Use:
Combine with volume or session filters for improved signal quality
Ideal for traders seeking clear entry/exit rules with minimal noise
Best on trending instruments and medium timeframes (USDJPY; Daily)
Tuga SupertrendDescription
This strategy uses the Supertrend indicator enhanced with commission and slippage filters to capture trends on the daily chart. It’s designed to work on any asset but is especially effective in markets with consistent movements.
Use the date inputs to set the backtest period (default: from January 1, 2018, through today, June 30, 2025).
The default input values are optimized for the daily chart. For other timeframes, adjust the parameters to suit the asset you’re testing.
Release Notes
June 30, 2025
• Updated default backtest period to end on June 30, 2025.
• Default commission adjusted to 0.1 %.
• Slippage set to 3 ticks.
• Default slippage set to 3 ticks.
• Simplified the strategy name to “Tuga Supertrend”.
Default Parameters
Parameter Default Value
Supertrend Period 10
Multiplier (Factor) 3
Commission 0.1 %
Slippage 3 ticks
Start Date January 1, 2018
End Date June 30, 2025
Liquidity Sweep Strategy v2 - Fixed Close LabelsThe Liquidity Sweep Strategy v2 is designed to detect stop-loss hunting behavior, commonly seen in institutional trading. It capitalizes on false breakouts beyond recent swing highs or lows (liquidity zones), which are followed by sharp reversals.
This strategy is particularly effective during high-volume liquidity grabs when markets trigger stop-loss clusters and then reverse direction — a phenomenon often referred to as a liquidity sweep or stop hunt
Ichimoku + RSI + VWMA Strategy Suite (w/ ATR SLTP)Ichimoku + RSI + VWMA indikatörleri kullanılarak üretilen seçmeli stratejiler.
TMNT3 [v5, Code Copilot] with PyramidCore Principles
Trend-Following Breakouts
Enters on clean price breakouts above the prior N-day high (System 1: 20 days; System 2: 55 days).
Exits on reversals through the prior M-day low (System 1: 10 days; System 2: 20 days).
Volatility-Based Stops
Uses the Average True Range (ATR) to set a dynamic stop-loss at
Stop = Entry Price ± (ATR×Multiplier)
Stop= Entry Price-(ATR×Multiplier)
Adapts to changing market noise—wider stops in volatile conditions, tighter in calm markets.
System 1 vs. System 2 Toggle
System 1 (20/10) for shorter, faster swing opportunities.
System 2 (55/20) for catching longer, more powerful trends.
Pyramiding into Winners
Scales into a position in fixed “units” (each risking a constant % of equity).
Adds an extra unit each time price extends by a set fraction of ATR (default 0.5× ATR), up to a configurable maximum (default 5 units).
Only increases exposure when the trend proves itself—managing risk while maximizing returns.
Strict Risk Management
Each unit carries its own ATR-based stop, ensuring no single leg blows out the account.
Default risk per unit is a small, fixed percentage of total equity (e.g. 1% per unit).
Visual Aids & Confirmation
Overlaid entry/exit channels and trend/exit lines for immediate context.
Optional on-chart labels and background shading to highlight active trade regimes.
Why It Works
Objectivity & Discipline: Rules-based entries, exits, and sizing remove emotional guesswork.
Adaptive to Market Conditions: ATR stops and pyramiding adapt to both calm and turbulent phases.
Scalable: Toggle between short and long breakout horizons to suit different assets or timeframes.
LANZ Strategy 1.0 [Backtest]🔷 LANZ Strategy 1.0 — Time-Based Session Trading with Smart Reversal Logic and Risk-Controlled Limit Orders
This backtest version of LANZ Strategy 1.0 brings precision to session-based trading by using directional confirmation, pre-defined risk parameters, and limit orders that execute overnight. Designed for the 1-hour timeframe, it allows traders to evaluate the system with configurable SL, TP, and risk settings in a fully automated environment.
🧠 Core Strategy Logic:
1. Directional Confirmation at 18:00 NY:
At 18:00 NY, the system compares the 08:00 open vs the 18:00 close:
If the direction matches the previous day, the signal is reversed.
If the direction differs, the current day's trend is kept.
This logic is designed to avoid momentum exhaustion and capture corrective reversals.
2. Entry Level Definition:
Based on the confirmed direction:
For BUY, the Low of the day is used as Entry Point (EP).
For SELL, the High of the day becomes EP.
The system plots a Stop Loss and Take Profit based on user-defined pip inputs (default: SL = 18 pips, TP = 54 pips → RR 1:3).
3. Time-Limited Entry Execution (LIMIT Orders):
Orders are sent after 18:00 NY and can be triggered anytime between 18:00 and 08:00 NY.
If EP is not touched before 08:00, the order is automatically cancelled.
4. Manual Close Feature:
If the trade is still open at the configured hour (default 09:00 NY), the system closes all positions, simulating realistic intraday exit scenarios.
5. Lot Size Calculation Based on Risk:
Lot size is dynamically calculated using the account size, risk percentage, and SL distance.
This ensures consistent risk exposure regardless of market volatility.
⚙️ Step-by-Step Flow:
08:00 NY → Captures the open of the day.
18:00 NY → Confirms direction and defines EP, SL, and TP.
After 18:00 NY → If conditions are met, a LIMIT order is placed at EP.
Between 18:00–08:00 NY → If price touches EP, the trade is executed.
At 08:00 NY → If EP wasn’t touched, the order is cancelled.
At Configured Manual Close Time (default 09:00 NY) → All open positions are force-closed if still active.
🧪 Backtest Settings:
Timeframe: 1-hour only
Order Type: strategy.entry() with limit=
SL/TP Configurable: Yes, in pips
Risk Input: % of capital per trade
Manual Close Time: Fully adjustable (default 09:00 NY)
👨💻 Credits:
Developed by LANZ
Strategy logic and trading concept built with clarity and precision.
Code structure and documentation by Kairos, your AI trading assistant.
Designed for high-confidence execution and clean backtesting performance.
Out of the Noise Intraday Strategy with VWAP [YuL]This is my (naive) implementation of "Beat the Market An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)" paper by Carlo Zarattini, Andrew Aziz, Andrea Barbon, so the credit goes to them.
It is supposed to run on SPY on 30-minute timeframe, there may be issues on other timeframes.
I've used settings that were used by the authors in the original paper to keep it close to the publication, but I understand that they are very aggressive and probably shouldn't be used like that.
Results are good, but not as good as they are stated in the paper (unsurprisingly?): returns are smaller and Sharpe is very low (which is actually weird given the returns and drawdown ratio), there are also margin calls if you enable margin check (and you should).
I have my own ideas of improvements which I will probably implement separately to keep this clean.
US30 Stealth StrategyOnly works on US30 (CAPITALCOM) 5 Minute chart
📈 Core Concept:
This is a trend-following strategy that captures strong market continuations by entering on:
The 3rd swing in the current trend,
Confirmed by a volume-verified engulfing candle,
With adaptive SL/TP and position sizing based on risk.
🧠 Entry Logic:
✅ Trend Filter
Uses a 50-period Simple Moving Average (SMA).
Buy only if price is above SMA → Uptrend
Sell only if price is below SMA → Downtrend
✅ Swing Count Logic
For buy: Wait for the 3rd higher low
For sell: Wait for the 3rd lower high
Uses a 5-bar lookback to detect highs/lows
This ensures you’re not buying early — but after trend is confirmed with structure.
✅ Engulfing Candle Confirmation
Bullish engulfing for buys
Bearish engulfing for sells
Candle must engulf previous bar completely (body logic)
✅ Volume Filter
Current candle volume must be greater than the 20-period volume average
Ensures trades only occur with institutional participation
✅ MA Slope Filter
Requires the slope of the 50 SMA over the last 3 candles to exceed 0.1
Avoids chop or flat trends
Adds momentum confirmation to the trade
✅ Session Filter (Time Filter)
Trades only executed between:
2:00 AM to 11:00 PM Oman Time (UTC+4)
Helps avoid overnight chop and illiquidity
📊 Position Sizing & Risk Management
✅ Smart SL (Adaptive Stop Loss)
SL is based on full size of the signal candle (including wick)
But if candle is larger than 25 points, SL is cut to half the size
This prevents oversized risk from long signals during volatile moves.
Mahnam BTC with breake outThis strategy is designed and coded specifically for trading Bitcoin in the 15-minute timeframe.
Of course, those who are skilled in coding can use it in other timeframes and currencies by changing its codes and personalizing it.
Of course, it is strongly recommended that people who want to use it first perform the necessary backtests or test this strategy on demo sites and then trade on the Tetri platform.
In this strategy, it only checks the entry and exit conditions and connects to the exchange using the API code and trades completely automatically.
This strategy determines the stop loss and take profit points on the exchange at the same time as entering the transaction and sets them.
///////////////////////// Code ////////////////////////////////
//@version=5
// Copyright (c) 2021-present, Alex Orekhov (everget)
//indicator('HalfTrend and TMA', overlay=true , max_lines_count = 500, max_labels_count = 500)
strategy(title='Mahnam BTC with breake out', overlay=true , max_bars_back=5000 , max_labels_count= 500 , max_boxes_count = 500,max_lines_count = 500, initial_capital=1000, currency = currency.USDT, default_qty_type=strategy.cash )
import PineCoders/Time/4
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
newyork = '0000-2400' // input.session(title='Session', defval='0000-2400')
time_newyork = time(timeframe.period, newyork)
///////////////////////////////////////////////////////////////////////////////////////////////////////////
// تعیین تاریخ شروع و پایان (بر حسب timestamp یونیکس)
// تنظیمات Input برای تاریخ شروع و پایان
startDate = input.time(timestamp('01 Jan 2025 00:00 UTC'), "📅 تاریخ شروع معاملات", inline="dateRange")
endDate = input.time(timestamp('31 Dec 2025 23:59 UTC'), "📅 تاریخ پایان معاملات", inline="dateRange")
// بررسی اینکه آیا زمان فعلی در بازه مجاز است یا خیر
isTradeEnabled = (time >= startDate) //and (time <= endDate)
///////////////////////////////////////////////////////////////////////////////////////////
// currentTime = time("15", "GMT+0")
// hourOfDay = hour(currentTime)
// notrade_hours1 = input.(12 , minval = 0 , maxval = 24 , title = "Hours Friday")
// notrade_hours2 = input.int(12 , minval = 0 , maxval = 24 , title = "Hours Monday")
////////////////////////////////////////////////////////////Holidays/////////////////////
// تعریف روزهای هفته
isSaturday = dayofweek == dayofweek.saturday //and hourOfDay > 12
isSunday = dayofweek == dayofweek.sunday
// isMonday = dayofweek == dayofweek.monday and hourOfDay < notrade_hours1
// isFriday = dayofweek == dayofweek.friday and hourOfDay > notrade_hours2
// رنگآمیزی پسزمینه برای شنبه (آبی کمرنگ) و یکشنبه (نارنجی کمرنگ)
bgcolor(isSaturday ? color.new(color.blue, 90) : isSunday ? color.new(color.orange, 90) : na)
//bgcolor(isMonday ? color.new(color.white, 90) : isFriday ? color.new(color.green, 90) : na)
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//تنظیمات پوزیشن
leverage = input.int(defval = 10 , title = "leverage" , minval = 1 , maxval = 20,step = 5 , group="Posistion Settings==========================================")
quantity = input.float(defval = 500 , title = "quantity" , minval = 1, group="Posistion Settings==========================================")
sl_manager = input.float(defval = 0.5 , step = 0.1 , title = "Risk Percent Of Capital", group="Posistion Settings==========================================")
persent_fee = input.float(defval = 0.05 , title = "Persent Fee Eexchange" , minval = 0 , maxval = 1,step = 0.01 , group="Posistion Settings==========================================")
position_type = input.string(defval = "Buy_And_Sell" , title = "Position_type" , options = , group="Posistion Settings==========================================" )
r_r_long = input.float(defval = 2 , step = 0.1 , title = "R - R =>", group="Posistion Settings==========================================")
r_r_short = r_r_long // input.float(defval = 1.8 , step = 0.1 , title = "r_r Short =>")
//////////////////////////////////////////////////////// END ROC /////////////////////////////////////
day_of_week = input.bool(false , title = "Trade in 7 days", group="Posistion Settings==========================================")
show_tp_sl_ent = true // input.bool(defval=true, title= "Show Tp Sl Ent Box", group="Posistion Settings==========================================")
show_qty = true // input.bool(defval = true , title = "Show Qty Label", group="Posistion Settings==========================================")
//////////////////////////////////////////////////////// Information Position ////////////////////////////////////////////////////
var short_is_open = false
var long_is_open = false
//variant for sell position
var sl1 = 0.0
var tp1 = 0.0
var ent1 = 0.0
var equity1 = 0.0
var qty1 = ""
//variant for buy position
var sl3 = 0.0
var qty2 = ""
var tp3 = 0.0
var ent2 = 0.0
var equity2 = 0.0
symbol = str.tostring(syminfo.basecurrency + "-" + syminfo.currency )
////////////////////////////////////////////////////////////////////////////////////////////////////////
var long_condition = false
var short_condition = false
persent_candel = 0.7 // input.float(defval = 0.7 , step = 0.1 , title = "درصد حرکت آخرین کندل", group="CANDEL Settings==========================================")
////////////////////////////////////////////////////////////////////////////////////////////////////////
amplitude = 2 // input.int(title='Amplitude', defval=2)
channelDeviation =2 //input.int(title='Channel Deviation', defval=2)
showChannels =true // input.bool(title='Show Channels', defval=true)
var int trend = 0
var int nextTrend = 0
var float maxLowPrice = nz(low , low)
var float minHighPrice = nz(high , high)
var float up = 0.0
var float down = 0.0
float atrHigh = 0.0
float atrLow = 0.0
float arrowUp = na
float arrowDown = na
len_atr = 130 // input.int(130 , title = "Len Half Trend")
atr2 = ta.atr(len_atr) / 2
dev = channelDeviation * atr2
highPrice = high
lowPrice = low
highma = ta.sma(high, amplitude)
lowma = ta.sma(low, amplitude)
if nextTrend == 1
maxLowPrice := math.max(lowPrice, maxLowPrice)
if highma < maxLowPrice and close < nz(low , low)
trend := 1
nextTrend := 0
minHighPrice := highPrice
minHighPrice
else
minHighPrice := math.min(highPrice, minHighPrice)
if lowma > minHighPrice and close > nz(high , high)
trend := 0
nextTrend := 1
maxLowPrice := lowPrice
maxLowPrice
if trend == 0
if not na(trend ) and trend != 0
up := na(down ) ? down : down
arrowUp := up - atr2
arrowUp
else
up := na(up ) ? maxLowPrice : math.max(maxLowPrice, up )
up
atrHigh := up + dev
atrLow := up - dev
atrLow
else
if not na(trend ) and trend != 1
down := na(up ) ? up : up
arrowDown := down + atr2
arrowDown
else
down := na(down ) ? minHighPrice : math.min(minHighPrice, down )
down
atrHigh := down + dev
atrLow := down - dev
atrLow
//////////////////////////////////////////////////////////////////////////////////////////////////////////
len_rsi = 14 // input.int(14, group = "RSI Setting=================================")
rsi = ta.rsi(close , len_rsi)
//////////////////////////////////////////////////////////////////////////////////
// محاسبات مربوط به تعیین خطوط حمایت و مقاومت و شکست آنها
show_ATR = input.bool(false)
lookback_15 = 4 // input.int(4, title = "====>Look Back 1H=====>", inline = "2", group = "Setting Pivot======================", tooltip = "Drawing support and resistance in time frame 15 min in selected look back")
pl60 = fixnan(ta.pivotlow( low , lookback_15 , lookback_15 ))
ph60 = fixnan(ta.pivothigh( high , lookback_15 , lookback_15 ))
plot(show_ATR ? pl60 : na , color = color.red)
plot(show_ATR ? ph60 : na , color = color.green)
//////////////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////
len_ema_fast_long = 2 // input.int(2)
sorce_tma_long = low // input.source(low)
ema_fast_long = ta.ema(sorce_tma_long , len_ema_fast_long)
len_ema_slow_long = 25 // input.int(25)
ema_slow_long = ta.ema(sorce_tma_long , len_ema_slow_long)
//**********************************
len_ema_fast_short = 2 // input.int(2)
sorce_tma_short = high // input.source(close)
ema_fast_short = ta.ema(sorce_tma_short , len_ema_fast_short)
len_ema_slow_short = 25 // input.int(25)
ema_slow_short = ta.ema(sorce_tma_short , len_ema_slow_short)
///////////////////////////////////////////////////////////////////////////////////////////////////////////
bars = 2 // input.int(9,title="Volume Previous bars to check")
//one_side = input.bool(false, title="Positive values only")
float volume_up = 0
float volume_down = 0
for i = 0 to bars
if (close >open )
volume_up:=volume_up+volume
else
volume_down:=volume_down+volume
total_up_down_vol= volume_up-volume_down
vol_bb = 8 // input.int(8)
vol_aa = 2 // input.int(2)
pivot_high_vol = fixnan(ta.pivothigh(total_up_down_vol , vol_bb , vol_aa ))
pivot_low_vol = fixnan(ta.pivotlow(total_up_down_vol , vol_bb , vol_aa ))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////
CLOSE = close
LOW = low
HIGH = high
//////////////////////////////////////////////////////////////////////////////////
//
//reg_trend_on = input(true, 'Activate Reg Trend Line')
length_bull_bear = 4 // input.int(defval= 4, title='🔹 Length Reg Trend line=', minval=1)
//
BullTrend_hist = 0.0
BearTrend_hist = 0.0
BullTrend = (CLOSE - ta.lowest(LOW, length_bull_bear)) / (ta.sma(ta.tr(true), length_bull_bear ))
BearTrend = (ta.highest(HIGH, length_bull_bear) - CLOSE) / (ta.sma(ta.tr(true), length_bull_bear ))
BearTrend2 = -1 * BearTrend
Trend = BullTrend - BearTrend
// plot columun
if BullTrend < 2
BullTrend_hist := BullTrend - 2
BullTrend_hist
if BearTrend2 > -2
BearTrend_hist := BearTrend2 + 2
BearTrend_hist
//alexgrover-Regression Line Formula
x = bar_index
y = Trend
x_ = ta.sma(x, length_bull_bear)
y_ = ta.sma(y, length_bull_bear)
mx = ta.stdev(x, length_bull_bear)
my = ta.stdev(y, length_bull_bear)
c = ta.correlation(x, y, length_bull_bear)
slope = c * (my / mx)
inter = y_ - slope * x_
reg_trend = x * slope + inter
/////////////////////////////////////////////////
long2 = true
short2 = true
close_H = request.security("" , "" , close )
open_H = request.security("" , "" , open )
if close_H > open_H and close_H > open_H
short2 := false
if close_H < open_H and close_H < open_H
long2 := false
nnn = 1.4 // input.float(1.4 , step = 0.1)
long_1 = BullTrend > nnn and ta.sma(reg_trend , 4 ) > ta.sma(reg_trend , 8 )
short_1 = BearTrend2 < -nnn and ta.sma(reg_trend , 4 ) < ta.sma(reg_trend , 8 )
///////////////////////////////////////////////////
lensig_mdi = 8 // input.int(8, title="ADX Smoothing", minval=1)
len_mdi = 2 // input.int(2, minval=1, title="DI Length")
up_mdi = ta.change(high)
down_mdi = -ta.change(low)
plusDM = na(up_mdi) ? na : (up_mdi > down_mdi and up_mdi > 0 ? up_mdi : 0)
minusDM = na(down_mdi) ? na : (down_mdi > up_mdi and down_mdi > 0 ? down_mdi : 0)
trur_mdi = ta.rma(ta.tr, len_mdi)
plus_mdi = fixnan(100 * ta.rma(plusDM, len_mdi) / trur_mdi)
minus_mdi = fixnan(100 * ta.rma(minusDM, len_mdi) / trur_mdi)
sum = plus_mdi + minus_mdi
adx = 100 * ta.rma(math.abs(plus_mdi - minus_mdi) / (sum == 0 ? 1 : sum), lensig_mdi)
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// تنظیمات SuperTrend
atrPeriod = 28 // input(28, title="ATR Period Super Trend")
factor = 3 // input(3.0, title="Multiplier")
= ta.supertrend(factor, atrPeriod)
// تعریف تایمفریمهای بالاتر
htf0 = "30" // input.timeframe("30", title="تایمفریم تأیید اول (1H)")
htf1 = "60" // input.timeframe("60", title=" ایمفریم تأیید دوم (1H)")
htf2 = "240" // input.timeframe("240", title="تایمفریم تأیید سوم (4H)")
// محاسبه SuperTrend در تایمفریمهای بالاتر
supertrend1 = request.security(syminfo.tickerid, htf0, supertrend)
direction1 = request.security(syminfo.tickerid, htf0, direction)
supertrend1H = request.security(syminfo.tickerid, htf1, supertrend )
direction1H = request.security(syminfo.tickerid, htf1, direction)
supertrend4H = request.security(syminfo.tickerid, htf2, supertrend )
direction4H = request.security(syminfo.tickerid, htf2, direction)
// شرایط ورود
Condition_supertrend_long = (direction1H > 0 or direction4H > 0 or direction1 > 0) and volume > fixnan(ta.pivotlow(volume , 16 , 2 ))
Condition_supertrend_short = (direction1H < 0 or direction4H < 0 or direction1 < 0) and volume > fixnan(ta.pivotlow(volume , 16 , 2 ))
//////////////////////////////////////////////////////////////////////////////////////////////////////////
open_4h = request.security("" , "240" , open )
close_4h = request.security("" , "240" , close )
//////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
if day_of_week == false
if isTradeEnabled == true and time == time_newyork and not isSaturday and not isSunday //and not isFriday and not isMonday
long_condition := long_is_open == false and short_is_open == false and total_up_down_vol > pivot_low_vol and rsi > 51 and rsi < 80
and math.abs(close - open) < (persent_candel/100) * close and ema_fast_long > ema_slow_long and high > ph60 and open < ph60 and long_1 == true and long2 == true
and plus_mdi > minus_mdi and Condition_supertrend_long == true and high > close_4h and close > atrHigh
short_condition := long_is_open == false and short_is_open == false and total_up_down_vol > pivot_low_vol and rsi < 49 and rsi > 20
and math.abs(close - open) < (persent_candel/100) * close and ema_fast_short < ema_slow_short and low < pl60 and open > pl60 and short_1 == true and short2 == true
and plus_mdi < minus_mdi and Condition_supertrend_short == true and low < close_4h and close < atrLow
if day_of_week == true
if isTradeEnabled == true and time == time_newyork
long_condition := long_is_open == false and short_is_open == false and total_up_down_vol > pivot_low_vol and rsi > 51 and rsi < 80
and math.abs(close - open) < (persent_candel/100) * close and ema_fast_long > ema_slow_long and high > ph60 and open < ph60 and long_1 == true and long2 == true
and plus_mdi > minus_mdi and Condition_supertrend_long == true and high > close_4h and close > atrHigh
short_condition := long_is_open == false and short_is_open == false and total_up_down_vol > pivot_low_vol and rsi < 49 and rsi > 20
and math.abs(close - open) < (persent_candel/100) * close and ema_fast_short < ema_slow_short and low < pl60 and open > pl60 and short_1 == true and short2 == true
and plus_mdi < minus_mdi and Condition_supertrend_short == true and low < close_4h and close < atrLow
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//تنظیمات استاپ سل پوزیشن شورت و لانگ بر اساس ATR
length_atr = 2 // input.int(title='Length', defval=2, minval=1, group = "StopLoss Setting=================================")
m = 0.9 // input.float(0.9,step = 0.1,title = 'Multiplier', group = "StopLoss Setting=================================")
show_atr = false // input.bool(false, group = "StopLoss Setting=================================")
src1_atr = high //input(high , title = "Stoploss Short")
src2_atr = low //input(low ,title = "Stoploss Long")
collong_atr = color.rgb(0,255,0,0)
colshort_atr = color.rgb(255,0,0,0)
a1 = (ta.sma(ta.tr(true), length_atr) * m) / 2 + (ta.wma(ta.tr(true), length_atr) * m) / 2
stop_loss_short = src1_atr + a1
stop_loss_long = src2_atr - a1
p1_atr1 = plot(show_atr ? stop_loss_long : na, title='ATR Short Stop Loss', color=colshort_atr, style=plot.style_circles)
p2_atr1 = plot(show_atr ? stop_loss_short : na, title='ATR Long Stop Loss', color=collong_atr, style=plot.style_circles)
/////////////////////////////////////////////////////////////////Start Stop Loss///////////////////////////////////////////////
/////////////////////////////////////////////////////////////////END Stop Loss///////////////////////////////////////////////
var total_long_trade = 0
var loss_long = 0
var profit_long = 0
var sood_pos_long = 0.00
var zarar_pos_long = 0.00
var kol_sood_long = 0.00
var total_short_trade = 0
var loss_short = 0
var profit_short = 0
var sood_pos_short = 0.00
var zarar_pos_short = 0.00
var kol_sood_short = 0.00
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// ━━━━━━━━━━━━━━━━━━ تنظیمات ورودی ━━━━━━━━━━━━━━━━━━
var int candlesToWait = 12 // input.int(1, "تعداد کندلهای انتظار پس از معامله", minval=1)
// ━━━━━━━━━━━━━━━━━━ شناسایی آخرین معامله ━━━━━━━━━━━━━━━━━━
var int lastTradeCloseBar = na
var bool isCoolDownOver = true
// اگر معاملهای بسته شد، شماره کندل آن را ذخیره کن
if strategy.closedtrades > 0 and (na(lastTradeCloseBar) or strategy.closedtrades != strategy.closedtrades )
lastTradeCloseBar := bar_index
isCoolDownOver := false
// بررسی آیا تعداد کندلهای موردنظر گذشته است؟
if not na(lastTradeCloseBar) and (bar_index - lastTradeCloseBar) >= candlesToWait
isCoolDownOver := true
bgcolor(isCoolDownOver ? na : color.new(color.red, 90), title="Cooldown Status")
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// تنظیمات دستورات لازم برای ارسال به صرافی جهت پوزیشن لانگ
//ADD_quantity = 1.5 // input.float(2 , title = "در صورت واگرایی ماجین رو چند بابر کنم؟")
if position_type == "Buy" or position_type == "Buy_And_Sell"
if long_condition and isCoolDownOver
ent2 := close
sl3 :=stop_loss_long - (stop_loss_long * (0.5 / leverage) / 100 )
tp3 := ent2 + ((ent2 - sl3) * r_r_long)
number_coin = ((quantity * leverage * sl_manager) / ((ent2 - sl3) *100))
equity2 := math.round ((number_coin * close ) , 3)
if equity2 > quantity * leverage
equity2 := quantity * leverage
//////////////////////////////////////////////////////////////////////////////////
if show_qty
label.new(bar_index , low , str.tostring(equity2) + "$" , color = color.rgb(0, 255, 0,0) , size = size.normal , style = label.style_label_up)
strategy.entry(id="buy", direction = strategy.long , qty=(equity2/close) )
if close >= 10 and close < 500
qty2 := str.tostring(math.round(equity2/close , 2))
else
qty2 := str.tostring(math.round(equity2/close , 0))
if close > 500
qty2 := str.tostring(math.round(equity2/close , 3 ))
if symbol == "AAVEUSDT"
qty2 := str.tostring(math.round(equity2/close , 1))
// ================/ برای باز کردن پوزیشن از این مقدار استفاده میکند /======================
message1 = '{"symbol":"'+symbol+'","type":"MARKET", "side":"BUY", "positionSide": "LONG", "quantity":"'+qty2+'","leverage": "'+str.tostring(leverage)+'","marginMode": "Isolated","botmix-action":"open-market-order-v2"}'
// message1 = '{ "side":"Ask","symbol":"'+symbol+'","tradeType":"Market","entrustVolume":"'+qty1+'","action":"Open","marginMode":"Isolated","leverage":"'+str.tostring(leverage)+'", "takerProfitPrice":"'+str.tostring(tp1)+'","stopLossPrice":"'+str.tostring(sl1)+'","botmix-action":"open-market-order" }'
// message1 = '{ "batchOrders": ,"botmix-action":"open-multiple-order" }'
alert(message1 , alert.freq_once_per_bar)
message2 = '{"symbol":"'+symbol+'","type":"LIMIT","side":"SELL", "positionSide": "LONG","delay": 5 ,"quantity":"'+qty2+'","price": "'+str.tostring(tp3)+'", "botmix-action":"open-market-order-v2"}'
alert(message2 , alert.freq_once_per_bar)
message3 = '{"symbol":"'+symbol+'","type":"STOP_MARKET","side":"SELL","positionSide": "LONG","delay": 10 ,"quantity":"'+qty2+'","price": "'+str.tostring(sl3)+'", "stopPrice": "'+str.tostring(sl3)+'","botmix-action":"open-market-order-v2"}'
alert(message3 , alert.freq_once_per_bar)
long_is_open := true
if show_tp_sl_ent
line.new(bar_index, tp3, bar_index + 15, tp3, xloc= xloc.bar_index, color= color.rgb(0, 255, 0,0 ), width = 1)
box.new(bar_index , tp3 , bar_index + 15 , ent2 ,bgcolor = color.rgb(0, 255, 0 , 90) , border_color = color.rgb(0, 255, 0 , 80) )
line.new(bar_index, (tp3 - ((tp3 - ent2) /2)), bar_index + 15, (tp3 - ((tp3 - ent2) /2)), xloc= xloc.bar_index, color= color.rgb(0, 17, 255), width = 2 , style = line.style_dashed)
line.new(bar_index, sl3, bar_index + 15, sl3, xloc= xloc.bar_index, color= color.rgb(255, 0, 0,0), width = 1)
box.new(bar_index , sl3 , bar_index + 15 , ent2 ,bgcolor = color.rgb(255, 0, 0, 90) , border_color = color.rgb(255, 0, 0 , 80) )
line.new(bar_index , ent2 , bar_index + 15 , ent2 , color = color.rgb(255, 255, 0, 0))
/////////////////////////////////////////////////////////
total_long_trade := total_long_trade + 1
if low <= sl3 and long_is_open == true
loss_long := loss_long + 1
zarar_pos_long := zarar_pos_long + (((ent2 - sl3) / ent2) * equity2)
if high >= tp3 and long_is_open == true
profit_long := profit_long + 1
sood_pos_long := sood_pos_long +(((tp3 - ent2) / ent2) * equity2)
kol_sood_long := sood_pos_long - zarar_pos_long
/////////////////////////////////////////////////////////////
if (low <= sl3 or high >= tp3) and long_is_open == true
long_is_open := false
strategy.exit( id = "buy" , from_entry = "buy" , limit = tp3 , stop = sl3 , qty_percent = 100 , comment_profit = "tp" , comment_loss = "sl" )
color_kol_pos_long = kol_sood_long >0 ? color.rgb(0,255,0) : color.rgb(255,0,0)
// //////////////////////LONG___ENNNDD//////////////////////////////////////////////////////////
// تظیمات دستورات لازم برای ارسال به صرافی جهت پوزیشن شورت
if position_type == "Sell" or position_type == "Buy_And_Sell"
if short_condition and isCoolDownOver
ent1 := close
sl1 :=stop_loss_short + (stop_loss_short * (0.5 / leverage) / 100 )
tp1 := ent1 - ((sl1 - ent1 ) * r_r_short)
number_coin = ((quantity * leverage * sl_manager) / ((sl1 - ent1) *100))
equity1 := math.round ((number_coin * close ) , 3)
if equity1 > quantity * leverage
equity1 := quantity * leverage
/////////////////////////////////////////////////////////////////////////////////////////
if show_qty
label.new(bar_index , high , str.tostring(equity1) + "$" , color = color.rgb(255, 0, 0,0) , size = size.normal , style = label.style_label_down)
strategy.entry(id="sell", direction = strategy.short, qty=(equity1/close) )
if close >= 10 and close < 500
qty1 := str.tostring(math.round(equity1/close , 2))
else
qty1 := str.tostring(math.round(equity1/close , 0))
if close > 500
qty1 := str.tostring(math.round(equity1/close , 3))
if symbol == "AAVEUSDT"
qty1 := str.tostring(math.round(equity1/close , 1))
// ================/ برای باز کردن پوزیشن از این مقدار استفاده میکند /======================
message1 = '{"symbol":"'+symbol+'","type":"MARKET", "side":"SELL", "positionSide": "SHORT", "quantity":"'+qty1+'","leverage": "'+str.tostring(leverage)+'","marginMode": "Isolated","botmix-action":"open-market-order-v2"}'
// message1 = '{ "side":"Ask","symbol":"'+symbol+'","tradeType":"Market","entrustVolume":"'+qty1+'","action":"Open","marginMode":"Isolated","leverage":"'+str.tostring(leverage)+'", "takerProfitPrice":"'+str.tostring(tp1)+'","stopLossPrice":"'+str.tostring(sl1)+'","botmix-action":"open-market-order" }'
// message1 = '{ "batchOrders": ,"botmix-action":"open-multiple-order" }'
alert(message1 , alert.freq_once_per_bar)
message2 = '{"symbol":"'+symbol+'","type":"LIMIT","side":"BUY", "positionSide": "SHORT","delay": 5 ,"quantity":"'+qty1+'","price": "'+str.tostring(tp1)+'", "botmix-action":"open-market-order-v2"}'
alert(message2 , alert.freq_once_per_bar)
message3 = '{"symbol":"'+symbol+'","type":"STOP_MARKET","side":"BUY","positionSide": "SHORT","delay": 10 ,"quantity":"'+qty1+'","price": "'+str.tostring(sl1)+'", "stopPrice": "'+str.tostring(sl1)+'","botmix-action":"open-market-order-v2"}'
alert(message3 , alert.freq_once_per_bar)
short_is_open := true
if show_tp_sl_ent
line.new(bar_index, tp1, bar_index + 15, tp1, xloc= xloc.bar_index, color= color.rgb(0, 255, 0,0 ), width = 1)
box.new(bar_index , tp1 , bar_index + 15 , ent1 ,bgcolor = color.rgb(0, 255, 0 , 90) , border_color = color.rgb(0, 255, 0 , 80) )
line.new(bar_index, (tp1+((ent1 - tp1)/2)), bar_index + 15, (tp1+((ent1 - tp1)/2)), xloc= xloc.bar_index, color= color.rgb(4, 0, 255), width = 2 , style= line.style_dashed)
line.new(bar_index, sl1, bar_index + 15, sl1, xloc= xloc.bar_index, color= color.rgb(255, 0, 0,50), width = 1)
box.new(bar_index , sl1 , bar_index + 15 , ent1 ,bgcolor = color.rgb(255, 0, 0, 90) , border_color = color.rgb(255, 0, 0 , 80) )
line.new(bar_index , ent1 , bar_index + 15 , ent1 , color = color.rgb(255, 255, 0,0))
////////////////////////////////////////////////////////////////////////////////////
total_short_trade := total_short_trade + 1
if high >= sl1 and short_is_open == true
loss_short := loss_long + 1
zarar_pos_short := zarar_pos_short + (((sl1 - ent1) / ent1) * equity1)
if low <= tp1 and short_is_open == true
profit_short := profit_short + 1
sood_pos_short := sood_pos_short +(((ent1 - tp1) / ent1) * equity1)
kol_sood_short := sood_pos_short - zarar_pos_short
///////////////////////////////////////////////////////////////////////////////////
if (high >= sl1 or low <= tp1 ) and short_is_open == true
short_is_open := false
strategy.exit( id = "sellext1" , from_entry = "sell" , limit = tp1 , stop = sl1 , qty_percent = 100 , comment_profit = "tp" , comment_loss = "sl" )
color_kol_pos_short = kol_sood_short > 0 ? color.rgb(0,255,0) : color.rgb(255,0,0)
////////////////////////////////////////////////////////////////////////////////////////////
kol_trade = loss_short + loss_long + profit_long + profit_short
/////////////////////SHORT___ENNNDD//////////////////////////////////////////////////////
closed_trades = (loss_short + loss_long + profit_long + profit_short) // strategy.closedtrades
kolfee = (closed_trades * quantity * leverage * persent_fee) / 100
net_profit = math.round((kol_sood_short + kol_sood_long) , 2 ) - kolfee
net_percent = math.round((net_profit / quantity) * 100 , 2)
win_rate = math.round(((profit_long + profit_short) / kol_trade) * 100 , 2) //math.round((strategy.wintrades / strategy.closedtrades) * 100 , 2)
ending = math.round((quantity + net_profit) , 2)
profit_factor = math.round((sood_pos_long + sood_pos_short) / math.abs(zarar_pos_long + zarar_pos_short) , 2)
drow_down = math.round((strategy.max_drawdown / quantity) * 100, 2 )
show_reportTabel = input.bool(true)
if show_reportTabel
table_color = color.rgb(0, 0, 0)
var table result_table = table.new(position.top_right, 30, 40, bgcolor=color.rgb(255,255,255,0), frame_color=color.rgb(0, 0, 0,0), frame_width=1, border_width=2)
table.cell(result_table , column = 0 , row = 0 , text = "TEST BTC with breake out: " + str.tostring(kol_trade) , bgcolor = table_color , text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 1 , row = 0 , text = "starting: " + str.tostring(quantity) + "$" , bgcolor = table_color, text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 2 , row = 0 , text = "Net Profit: " + str.tostring(net_profit) + "$: " + " fee = " + str.tostring(kolfee) , bgcolor = table_color, text_color = net_profit > 0 ? color.rgb(0,255,0,0) : color.rgb(255,0,0,0))
table.cell(result_table , column = 0 , row = 1 , text = "Win Rate: " + str.tostring(win_rate) + "%" , bgcolor = table_color, text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 1 , row = 1 , text = "Ending: " + str.tostring(ending) + "$" , bgcolor = table_color, text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 2 , row = 1 , text = "Profit Factor: " + str.tostring(profit_factor) , bgcolor = table_color, text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 3 , row = 0 , text = "Net Percent: " + str.tostring(net_percent) + "%" , bgcolor = table_color, text_color = net_percent > 0 ? color.rgb(0,255,0,0) : color.rgb(255,0,0,0))
table.cell(result_table , column = 3 , row = 1 , text = "Draw Down: " + str.tostring(drow_down) + "%" , bgcolor = table_color, text_color = color.rgb(255,255,255,0))
table.cell(result_table , column = 4 , row = 0 , text = "Stop: " + "Short =" + str.tostring(loss_short)+ " " +"Long =" + str.tostring(loss_long) , bgcolor = table_color, text_color = color.rgb(255,0,0,0))
table.cell(result_table , column = 4 , row = 1 , text = "TP: " + "Short =" + str.tostring(profit_short)+ " " +"Long =" + str.tostring(profit_long) , bgcolor = table_color, text_color = color.rgb(0,255,0,0))
table.cell(result_table , column = 5 , row = 0 , text = "Short: " + "sood =" + str.tostring(math.round(sood_pos_short,2)) + " " + "Zarar =" + str.tostring(math.round(zarar_pos_short,2)) , bgcolor = table_color, text_color = color.rgb(0,255,0,0))
table.cell(result_table , column = 5 , row = 1 , text = "Long: " + "sood =" + str.tostring(math.round(sood_pos_long,2)) + " " + "Zarar =" + str.tostring(math.round(zarar_pos_long,2)) , bgcolor = table_color, text_color = color.rgb(0,255,0,0))
table.cell(result_table , column = 6 , row = 0 , text = "Kol Sood Short: " + "Short =" + str.tostring(math.round(kol_sood_short,2)) , bgcolor = table_color, text_color = color_kol_pos_short)
table.cell(result_table , column = 6 , row = 1 , text = "Kol Sood Long: " + "LONG =" + str.tostring(math.round(kol_sood_long,2)) , bgcolor = table_color, text_color = color_kol_pos_long)
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// //////////////////////////////////////////////////////////////////////////////////////////////////////
// // ********** تنظیمات **********
// show_monthly_Report = input.bool(false, "نمایش گزارش ماهیانه")
// exchange_fee_percent = 0.05 / 100 // input.float(0.05, "کارمزد صرافی (%)", step=0.01) / 100
// indicator_name = 'BTC with breake out' // input.string("تحلیلگر حرفه ای - گزارش ماهیانه", "عنوان اندیکاتور")
// show_total_row = true // input.bool(true, "نمایش سطر جمع کل")
// // ********** ایجاد جدول **********
// var table monthlyReport = table.new(position = position.top_right, columns = 6,rows = 21,bgcolor = color.rgb(33, 33, 33),
// border_width = 2,border_color = color.rgb(80, 80, 80),frame_width = 1,frame_color = color.rgb(50, 50, 50))
// // ********** متغیرهای ماهیانه **********
// var int currentMonth = na
// var int monthTrades = 0
// var int monthWinningTrades = 0
// var float totalFees = 0.0
// var float monthNetProfit = 0.0
// // ********** متغیرهای جمع کل **********
// var float totalAllTrades = 0.0
// var float totalAllFees = 0.0
// var float totalAllNetProfit = 0.0
// var int totalAllWinningTrades = 0
// var int totalAllMonths = 0
// // ********** تشخیص تغییر ماه **********
// isNewMonth = ta.change(month) or ta.change(year)
// // ********** محاسبات معاملات **********
// tradeClosed = strategy.closedtrades > strategy.closedtrades
// if tradeClosed
// lastTradeIndex = strategy.closedtrades - 1
// tradeSize = math.abs(strategy.closedtrades.size(lastTradeIndex) * strategy.closedtrades.exit_price(lastTradeIndex))
// tradeFee = tradeSize * exchange_fee_percent
// totalFees := totalFees + tradeFee
// tradeProfit = strategy.closedtrades.profit(lastTradeIndex)
// monthNetProfit := monthNetProfit + tradeProfit
// monthTrades := monthTrades + 1
// if tradeProfit > 0
// monthWinningTrades := monthWinningTrades + 1
// // ********** مدیریت گزارش ماهیانه **********
// if isNewMonth and show_monthly_Report and not na(currentMonth)
// // محاسبات ماهانه
// grossProfit = monthNetProfit
// netProfit = grossProfit - totalFees
// winRate = monthTrades > 0 ? (monthWinningTrades/monthTrades)*100 : 0
// // به روزرسانی جمع کل
// totalAllTrades := totalAllTrades + monthTrades
// totalAllFees := totalAllFees + totalFees
// totalAllNetProfit := totalAllNetProfit + netProfit
// totalAllWinningTrades := totalAllWinningTrades + monthWinningTrades
// totalAllMonths := totalAllMonths + 1
// // نمایش در جدول
// row = (month % 12 == 0 ? 12 : month % 12) + 2 // +2 برای جا دادن سطرهای عنوان
// monthName = str.tostring(year ) + "-" + str.tostring(month , "00")
// table.cell(monthlyReport, 0, row, monthName, text_color=color.white)
// table.cell(monthlyReport, 1, row, str.tostring(monthTrades), text_color=color.white)
// table.cell(monthlyReport, 2, row, str.tostring(grossProfit, "0.00") + " $")
// table.cell(monthlyReport, 3, row, str.tostring(totalFees, "0.00") + " $")
// table.cell(monthlyReport, 4, row, str.tostring(netProfit, "0.00") + " $")
// table.cell(monthlyReport, 5, row, str.tostring(winRate, "1.0") + "%")
// // رنگ آمیزی سود/زیان
// textColor = netProfit >= 0 ? color.rgb(0, 200, 0) : color.rgb(200, 0, 0)
// for i = 2 to 5
// table.cell_set_text_color(monthlyReport, i, row, textColor)
// // ********** سطر جمع کل **********
// if show_monthly_Report and show_total_row and totalAllMonths > 0
// totalWinRate = totalAllTrades > 0 ? (totalAllWinningTrades/totalAllTrades)*100 : 0
// table.cell(monthlyReport, 0, 15, "جمع کل (" + str.tostring(totalAllMonths) + " ماه)",
// text_color=color.yellow,
// bgcolor=color.rgb(50, 50, 50),
// width=6)
// table.cell(monthlyReport, 1, 15, str.tostring(totalAllTrades),
// text_color=color.yellow,
// bgcolor=color.rgb(50, 50, 50))
// table.cell(monthlyReport, 2, 15, str.tostring(totalAllNetProfit + totalAllFees, "0.00") + " $",
// text_color=color.yellow,
// bgcolor=color.rgb(50, 50, 50))
// table.cell(monthlyReport, 3, 15, str.tostring(totalAllFees, "0.00") + " $",
// text_color=color.yellow,
// bgcolor=color.rgb(50, 50, 50))
// table.cell(monthlyReport, 4, 15, str.tostring(totalAllNetProfit, "0.00") + " $",
// text_color = totalAllNetProfit >= 0 ? color.green : color.red,
// bgcolor=color.rgb(50, 50, 50))
// table.cell(monthlyReport, 5, 15, str.tostring(totalWinRate, "1.0") + "%",
// text_color=color.yellow,
// bgcolor=color.rgb(50, 50, 50))
// // ********** ریست ماهیانه **********
// if isNewMonth
// currentMonth := month
// monthTrades := 0
// monthWinningTrades := 0
// totalFees := 0.0
// monthNetProfit := 0.0
// // ********** عنوانهای جدول **********
// if barstate.isfirst and show_monthly_Report
// // عنوان اصلی (یکپارچه در سطر اول)
// table.cell(
// monthlyReport,
// column = 4, // ستون شروع (0 = اولین ستون)
// row = 0, // ردیف 0 (اولین ردیف)
// text = indicator_name,
// bgcolor = color.rgb(0, 0, 0),
// text_size = size.small,
// text_color = color.rgb(255,255,0),
// width = 12, // گسترش روی تمام 6 ستون
// height = 4 // ارتفاع بیشتر برای وضوح بهتر
// )
// // عنوان ستونها (در ردیف دوم)
// headers = array.from("ماه", "تعداد", "سود ناخالص", "کارمزد", "سود خالص", "نرخ برد")
// for i = 0 to 5
// table.cell(
// monthlyReport,
// column = i,
// row = 1, // ردیف بعد از عنوان اصلی
// text = array.get(headers, i),
// text_color = color.white,
// bgcolor = color.rgb(60, 60, 60),
// width = 1 // عرض معمولی برای هر ستون
// )
Strategi Ichimoku UniversalWe use Ichimoku as the only trend and signal filter, with a bit of "price action" logic from the candles.
📌 BUY rule:
1.Close above the cloud (Kumo) → uptrend
2.Bullish candle (close > open)
3.Tenkan > Kijun → strong upward momentum
📌 SELL rule:
1.Close below Kumo → downtrend
2.Bearish candle (close < open)
3.Tenkan < Kijun
Long/Short/Exit/Risk management Strategy # LongShortExit Strategy Documentation
## Overview
The LongShortExit strategy is a versatile trading system for TradingView that provides complete control over entry, exit, and risk management parameters. It features a sophisticated framework for managing long and short positions with customizable profit targets, stop-loss mechanisms, partial profit-taking, and trailing stops. The strategy can be enhanced with continuous position signals for visual feedback on the current trading state.
## Key Features
### General Settings
- **Trading Direction**: Choose to trade long positions only, short positions only, or both.
- **Max Trades Per Day**: Limit the number of trades per day to prevent overtrading.
- **Bars Between Trades**: Enforce a minimum number of bars between consecutive trades.
### Session Management
- **Session Control**: Restrict trading to specific times of the day.
- **Time Zone**: Specify the time zone for session calculations.
- **Expiration**: Optionally set a date when the strategy should stop executing.
### Contract Settings
- **Contract Type**: Select from common futures contracts (MNQ, MES, NQ, ES) or custom values.
- **Point Value**: Define the dollar value per point movement.
- **Tick Size**: Set the minimum price movement for accurate calculations.
### Visual Signals
- **Continuous Position Signals**: Implement 0 to 1 visual signals to track position states.
- **Signal Plotting**: Customize color and appearance of position signals.
- **Clear Visual Feedback**: Instantly see when entry conditions are triggered.
### Risk Management
#### Stop Loss and Take Profit
- **Risk Type**: Choose between percentage-based, ATR-based, or points-based risk management.
- **Percentage Mode**: Set SL/TP as a percentage of entry price.
- **ATR Mode**: Set SL/TP as a multiple of the Average True Range.
- **Points Mode**: Set SL/TP as a fixed number of points from entry.
#### Advanced Exit Features
- **Break-Even**: Automatically move stop-loss to break-even after reaching specified profit threshold.
- **Trailing Stop**: Implement a trailing stop-loss that follows price movement at a defined distance.
- **Partial Profit Taking**: Take partial profits at predetermined price levels:
- Set first partial exit point and percentage of position to close
- Set second partial exit point and percentage of position to close
- **Time-Based Exit**: Automatically exit a position after a specified number of bars.
#### Win/Loss Streak Management
- **Streak Cutoff**: Automatically pause trading after a series of consecutive wins or losses.
- **Daily Reset**: Option to reset streak counters at the start of each day.
### Entry Conditions
- **Source and Value**: Define the exact price source and value that triggers entries.
- **Equals Condition**: Entry signals occur when the source exactly matches the specified value.
### Performance Analytics
- **Real-Time Stats**: Track important performance metrics like win rate, P&L, and largest wins/losses.
- **Visual Feedback**: On-chart markers for entries, exits, and important events.
### External Integration
- **Webhook Support**: Compatible with TradingView's webhook alerts for automated trading.
- **Cross-Platform**: Connect to external trading systems and notification platforms.
- **Custom Order Execution**: Implement advanced order flows through external services.
## How to Use
### Setup Instructions
1. Add the script to your TradingView chart.
2. Configure the general settings based on your trading preferences.
3. Set session trading hours if you only want to trade specific times.
4. Select your contract specifications or customize for your instrument.
5. Configure risk parameters:
- Choose your preferred risk management approach
- Set appropriate stop-loss and take-profit levels
- Enable advanced features like break-even, trailing stops, or partial profit taking as needed
6. Define entry conditions:
- Select the price source (such as close, open, high, or an indicator)
- Set the specific value that should trigger entries
### Entry Condition Examples
- **Example 1**: To enter when price closes exactly at a whole number:
- Long Source: close
- Long Value: 4200 (for instance, to enter when price closes exactly at 4200)
- **Example 2**: To enter when an indicator reaches a specific value:
- Long Source: ta.rsi(close, 14)
- Long Value: 30 (triggers when RSI equals exactly 30)
### Best Practices
1. **Always backtest thoroughly** before using in live trading.
2. **Start with conservative risk settings**:
- Small position sizes
- Reasonable stop-loss distances
- Limited trades per day
3. **Monitor and adjust**:
- Use the performance table to track results
- Adjust parameters based on how the strategy performs
4. **Consider market volatility**:
- Use ATR-based stops during volatile periods
- Use fixed points during stable markets
## Continuous Position Signals Implementation
The LongShortExit strategy can be enhanced with continuous position signals to provide visual feedback about the current position state. These signals can help you track when the strategy is in a long or short position.
### Adding Continuous Position Signals
Add the following code to implement continuous position signals (0 to 1):
```pine
// Continuous position signals (0 to 1)
var float longSignal = 0.0
var float shortSignal = 0.0
// Update position signals based on your indicator's conditions
longSignal := longCondition ? 1.0 : 0.0
shortSignal := shortCondition ? 1.0 : 0.0
// Plot continuous signals
plot(longSignal, title="Long Signal", color=#00FF00, linewidth=2, transp=0, style=plot.style_line)
plot(shortSignal, title="Short Signal", color=#FF0000, linewidth=2, transp=0, style=plot.style_line)
```
### Benefits of Continuous Position Signals
- Provides clear visual feedback of current position state (long/short)
- Signal values stay consistent (0 or 1) until condition changes
- Can be used for additional calculations or alert conditions
- Makes it easier to track when entry conditions are triggered
### Using with Custom Indicators
You can adapt the continuous position signals to work with any custom indicator by replacing the condition with your indicator's logic:
```pine
// Example with moving average crossover
longSignal := fastMA > slowMA ? 1.0 : 0.0
shortSignal := fastMA < slowMA ? 1.0 : 0.0
```
## Webhook Integration
The LongShortExit strategy is fully compatible with TradingView's webhook alerts, allowing you to connect your strategy to external trading platforms, brokers, or custom applications for automated trading execution.
### Setting Up Webhooks
1. Create an alert on your chart with the LongShortExit strategy
2. Enable the "Webhook URL" option in the alert dialog
3. Enter your webhook endpoint URL (from your broker or custom trading system)
4. Customize the alert message with relevant information using TradingView variables
### Webhook Message Format Example
```json
{
"strategy": "LongShortExit",
"action": "{{strategy.order.action}}",
"price": "{{strategy.order.price}}",
"quantity": "{{strategy.position_size}}",
"time": "{{time}}",
"ticker": "{{ticker}}",
"position_size": "{{strategy.position_size}}",
"position_value": "{{strategy.position_value}}",
"order_id": "{{strategy.order.id}}",
"order_comment": "{{strategy.order.comment}}"
}
```
### TradingView Alert Condition Examples
For effective webhook automation, set up these alert conditions:
#### Entry Alert
```
{{strategy.position_size}} != {{strategy.position_size}}
```
#### Exit Alert
```
{{strategy.position_size}} < {{strategy.position_size}} or {{strategy.position_size}} > {{strategy.position_size}}
```
#### Partial Take Profit Alert
```
strategy.order.comment contains "Partial TP"
```
### Benefits of Webhook Integration
- **Automated Trading**: Execute trades automatically through supported brokers
- **Cross-Platform**: Connect to custom trading bots and applications
- **Real-Time Notifications**: Receive trade signals on external platforms
- **Data Collection**: Log trade data for further analysis
- **Custom Order Management**: Implement advanced order types not available in TradingView
### Compatible External Applications
- Trading bots and algorithmic trading software
- Custom order execution systems
- Discord, Telegram, or Slack notification systems
- Trade journaling applications
- Risk management platforms
### Implementation Recommendations
- Test webhook delivery using a free service like webhook.site before connecting to your actual trading system
- Include authentication tokens or API keys in your webhook URL or payload when required by your external service
- Consider implementing confirmation mechanisms to verify trade execution
- Log all webhook activities for troubleshooting and performance tracking
## Strategy Customization Tips
### For Scalping
- Set smaller profit targets (1-3 points)
- Use tighter stop-losses
- Enable break-even feature after small profit
- Set higher max trades per day
### For Day Trading
- Use moderate profit targets
- Implement partial profit taking
- Enable trailing stops
- Set reasonable session trading hours
### For Swing Trading
- Use longer-term charts
- Set wider stops (ATR-based often works well)
- Use higher profit targets
- Disable daily streak reset
## Common Troubleshooting
### Low Win Rate
- Consider widening stop-losses
- Verify that entry conditions aren't triggering too frequently
- Check if the equals condition is too restrictive; consider small tolerances
### Missing Obvious Trades
- The equals condition is extremely precise. Price must exactly match the specified value.
- Consider using floating-point precision for more reliable triggers
### Frequent Stop-Outs
- Try ATR-based stops instead of fixed points
- Increase the stop-loss distance
- Enable break-even feature to protect profits
## Important Notes
- The exact equals condition is strict and may result in fewer trade signals compared to other conditions.
- For instruments with decimal prices, exact equality might be rare. Consider the precision of your value.
- Break-even and trailing stop calculations are based on points, not percentage.
- Partial take-profit levels are defined in points distance from entry.
- The continuous position signals (0 to 1) provide valuable visual feedback but don't affect the strategy's trading logic directly.
- When implementing continuous signals, ensure they're aligned with the actual entry conditions used by the strategy.
---
*This strategy is for educational and informational purposes only. Always test thoroughly before using with real funds.*
Timeshifter Triple Timeframe Strategy w/ SessionsOverview
The "Enhanced Timeshifter Triple Timeframe Strategy with Session Filtering" is a sophisticated trading strategy designed for the TradingView platform. It integrates multiple technical indicators across three different timeframes and allows traders to customize their trading Sessions. This strategy is ideal for traders who wish to leverage multi-timeframe analysis and session-based trading to enhance their trading decisions.
Features
Multi-Timeframe Analysis and direction:
Higher Timeframe: Set to a daily timeframe by default, providing a broader view of market trends.
Trading Timeframe: Automatically set to the current chart timeframe, ensuring alignment with the trader's primary analysis period.
Lower Timeframe: Set to a 15-minute timeframe by default, offering a granular view for precise entry and exit points.
Indicator Selection:
RMI (Relative Momentum Index): Combines RSI and MFI to gauge market momentum.
TWAP (Time Weighted Average Price): Provides an average price over a specified period, useful for identifying trends.
TEMA (Triple Exponential Moving Average): Reduces lag and smooths price data for trend identification.
DEMA (Double Exponential Moving Average): Similar to TEMA, it reduces lag and provides a smoother trend line.
MA (Moving Average): A simple moving average for basic trend analysis.
MFI (Money Flow Index): Measures the flow of money into and out of a security, useful for identifying overbought or oversold conditions.
VWMA (Volume Weighted Moving Average): Incorporates volume data into the moving average calculation.
PSAR (Parabolic SAR): Identifies potential reversals in price movement.
Session Filtering:
London Session: Trade during the London market hours (0800-1700 GMT+1).
New York Session: Trade during the New York market hours (0800-1700 GMT-5).
Tokyo Session: Trade during the Tokyo market hours (0900-1800 GMT+9).
Users can select one or multiple sessions to align trading with specific market hours.
Trade Direction:
Long: Only long trades are permitted.
Short: Only short trades are permitted.
Both: Both long and short trades are permitted, providing flexibility based on market conditions.
ADX Confirmation:
ADX (Average Directional Index): An optional filter to confirm the strength of a trend before entering a trade.
How to Use the Script
Setup:
Add the script to your TradingView chart.
Customize the input parameters according to your trading preferences and strategy requirements.
Indicator Selection:
Choose the primary indicator you wish to use for generating trading signals from the dropdown menu.
Enable or disable the ADX confirmation based on your preference for trend strength analysis.
Session Filtering:
Select the trading sessions you wish to trade in. You can choose one or multiple Sessions based on your trading strategy and market focus.
Trade Direction:
Set your preferred trade direction (Long, Short, or Both) to align with your market outlook and risk tolerance. You can use this feature to gauge the market and understand the possible directions.
Tips for Profitable and Safe Trading:
Recommended Timeframes Combination:
LT: 1m , CT: 5m, HT: 1H
LT: 1-5m , CT: 15m, HT: 4H
LT: 5-15m , CT: 4H, HT: 1W
Backtesting:
Always backtest the strategy on historical data to understand its performance under various market conditions.
Adjust the parameters based on backtesting results to optimize the strategy for your specific trading style.
Risk Management:
Use appropriate risk management techniques, such as setting stop-loss and take-profit levels, to protect your capital.
Avoid over-leveraging and ensure that you are trading within your risk tolerance.
Market Analysis:
Combine the script with other forms of market analysis, such as fundamental analysis or market sentiment, to make well-rounded trading decisions.
Stay informed about major economic events and news that could impact market volatility and trading sessions.
Continuous Monitoring:
Regularly monitor the strategy's performance and make adjustments as necessary.
Keep an eye on the results and settings for real-time statistics and ensure that the strategy aligns with current market conditions.
Education and Practice:
Continuously educate yourself on trading strategies and market dynamics.
Practice using the strategy in a demo account before applying it to live trading to gain confidence and understanding.
Anomalous Holonomy Field Theory🌌 Anomalous Holonomy Field Theory (AHFT) - Revolutionary Quantum Market Analysis
Where Theoretical Physics Meets Trading Reality
A Groundbreaking Synthesis of Differential Geometry, Quantum Field Theory, and Market Dynamics
🔬 THEORETICAL FOUNDATION - THE MATHEMATICS OF MARKET REALITY
The Anomalous Holonomy Field Theory represents an unprecedented fusion of advanced mathematical physics with practical market analysis. This isn't merely another indicator repackaging old concepts - it's a fundamentally new lens through which to view and understand market structure .
1. HOLONOMY GROUPS (Differential Geometry)
In differential geometry, holonomy measures how vectors change when parallel transported around closed loops in curved space. Applied to markets:
Mathematical Formula:
H = P exp(∮_C A_μ dx^μ)
Where:
P = Path ordering operator
A_μ = Market connection (price-volume gauge field)
C = Closed price path
Market Implementation:
The holonomy calculation measures how price "remembers" its journey through market space. When price returns to a previous level, the holonomy captures what has changed in the market's internal geometry. This reveals:
Hidden curvature in the market manifold
Topological obstructions to arbitrage
Geometric phase accumulated during price cycles
2. ANOMALY DETECTION (Quantum Field Theory)
Drawing from the Adler-Bell-Jackiw anomaly in quantum field theory:
Mathematical Formula:
∂_μ j^μ = (e²/16π²)F_μν F̃^μν
Where:
j^μ = Market current (order flow)
F_μν = Field strength tensor (volatility structure)
F̃^μν = Dual field strength
Market Application:
Anomalies represent symmetry breaking in market structure - moments when normal patterns fail and extraordinary opportunities arise. The system detects:
Spontaneous symmetry breaking (trend reversals)
Vacuum fluctuations (volatility clusters)
Non-perturbative effects (market crashes/melt-ups)
3. GAUGE THEORY (Theoretical Physics)
Markets exhibit gauge invariance - the fundamental physics remains unchanged under certain transformations:
Mathematical Formula:
A'_μ = A_μ + ∂_μΛ
This ensures our signals are gauge-invariant observables , immune to arbitrary market "coordinate changes" like gaps or reference point shifts.
4. TOPOLOGICAL DATA ANALYSIS
Using persistent homology and Morse theory:
Mathematical Formula:
β_k = dim(H_k(X))
Where β_k are the Betti numbers describing topological features that persist across scales.
🎯 REVOLUTIONARY SIGNAL CONFIGURATION
Signal Sensitivity (0.5-12.0, default 2.5)
Controls the responsiveness of holonomy field calculations to market conditions. This parameter directly affects the threshold for detecting quantum phase transitions in price action.
Optimization by Timeframe:
Scalping (1-5min): 1.5-3.0 for rapid signal generation
Day Trading (15min-1H): 2.5-5.0 for balanced sensitivity
Swing Trading (4H-1D): 5.0-8.0 for high-quality signals only
Score Amplifier (10-200, default 50)
Scales the raw holonomy field strength to produce meaningful signal values. Higher values amplify weak signals in low-volatility environments.
Signal Confirmation Toggle
When enabled, enforces additional technical filters (EMA and RSI alignment) to reduce false positives. Essential for conservative strategies.
Minimum Bars Between Signals (1-20, default 5)
Prevents overtrading by enforcing quantum decoherence time between signals. Higher values reduce whipsaws in choppy markets.
👑 ELITE EXECUTION SYSTEM
Execution Modes:
Conservative Mode:
Stricter signal criteria
Higher quality thresholds
Ideal for stable market conditions
Adaptive Mode:
Self-adjusting parameters
Balances signal frequency with quality
Recommended for most traders
Aggressive Mode:
Maximum signal sensitivity
Captures rapid market moves
Best for experienced traders in volatile conditions
Dynamic Position Sizing:
When enabled, the system scales position size based on:
Holonomy field strength
Current volatility regime
Recent performance metrics
Advanced Exit Management:
Implements trailing stops based on ATR and signal strength, with mode-specific multipliers for optimal profit capture.
🧠 ADAPTIVE INTELLIGENCE ENGINE
Self-Learning System:
The strategy analyzes recent trade outcomes and adjusts:
Risk multipliers based on win/loss ratios
Signal weights according to performance
Market regime detection for environmental adaptation
Learning Speed (0.05-0.3):
Controls adaptation rate. Higher values = faster learning but potentially unstable. Lower values = stable but slower adaptation.
Performance Window (20-100 trades):
Number of recent trades analyzed for adaptation. Longer windows provide stability, shorter windows increase responsiveness.
🎨 REVOLUTIONARY VISUAL SYSTEM
1. Holonomy Field Visualization
What it shows: Multi-layer quantum field bands representing market resonance zones
How to interpret:
Blue/Purple bands = Primary holonomy field (strongest resonance)
Band width = Field strength and volatility
Price within bands = Normal quantum state
Price breaking bands = Quantum phase transition
Trading application: Trade reversals at band extremes, breakouts on band violations with strong signals.
2. Quantum Portals
What they show: Entry signals with recursive depth patterns indicating momentum strength
How to interpret:
Upward triangles with portals = Long entry signals
Downward triangles with portals = Short entry signals
Portal depth = Signal strength and expected momentum
Color intensity = Probability of success
Trading application: Enter on portal appearance, with size proportional to portal depth.
3. Field Resonance Bands
What they show: Fibonacci-based harmonic price zones where quantum resonance occurs
How to interpret:
Dotted circles = Minor resonance levels
Solid circles = Major resonance levels
Color coding = Resonance strength
Trading application: Use as dynamic support/resistance, expect reactions at resonance zones.
4. Anomaly Detection Grid
What it shows: Fractal-based support/resistance with anomaly strength calculations
How to interpret:
Triple-layer lines = Major fractal levels with high anomaly probability
Labels show: Period (H8-H55), Price, and Anomaly strength (φ)
⚡ symbol = Extreme anomaly detected
● symbol = Strong anomaly
○ symbol = Normal conditions
Trading application: Expect major moves when price approaches high anomaly levels. Use for precise entry/exit timing.
5. Phase Space Flow
What it shows: Background heatmap revealing market topology and energy
How to interpret:
Dark background = Low market energy, range-bound
Purple glow = Building energy, trend developing
Bright intensity = High energy, strong directional move
Trading application: Trade aggressively in bright phases, reduce activity in dark phases.
📊 PROFESSIONAL DASHBOARD METRICS
Holonomy Field Strength (-100 to +100)
What it measures: The Wilson loop integral around price paths
>70: Strong positive curvature (bullish vortex)
<-70: Strong negative curvature (bearish collapse)
Near 0: Flat connection (range-bound)
Anomaly Level (0-100%)
What it measures: Quantum vacuum expectation deviation
>70%: Major anomaly (phase transition imminent)
30-70%: Moderate anomaly (elevated volatility)
<30%: Normal quantum fluctuations
Quantum State (-1, 0, +1)
What it measures: Market wave function collapse
+1: Bullish eigenstate |↑⟩
0: Superposition (uncertain)
-1: Bearish eigenstate |↓⟩
Signal Quality Ratings
LEGENDARY: All quantum fields aligned, maximum probability
EXCEPTIONAL: Strong holonomy with anomaly confirmation
STRONG: Good field strength, moderate anomaly
MODERATE: Decent signals, some uncertainty
WEAK: Minimal edge, high quantum noise
Performance Metrics
Win Rate: Rolling performance with emoji indicators
Daily P&L: Real-time profit tracking
Adaptive Risk: Current risk multiplier status
Market Regime: Bull/Bear classification
🏆 WHY THIS CHANGES EVERYTHING
Traditional technical analysis operates on 100-year-old principles - moving averages, support/resistance, and pattern recognition. These work because many traders use them, creating self-fulfilling prophecies.
AHFT transcends this limitation by analyzing markets through the lens of fundamental physics:
Markets have geometry - The holonomy calculations reveal this hidden structure
Price has memory - The geometric phase captures path-dependent effects
Anomalies are predictable - Quantum field theory identifies symmetry breaking
Everything is connected - Gauge theory unifies disparate market phenomena
This isn't just a new indicator - it's a new way of thinking about markets . Just as Einstein's relativity revolutionized physics beyond Newton's mechanics, AHFT revolutionizes technical analysis beyond traditional methods.
🔧 OPTIMAL SETTINGS FOR MNQ 10-MINUTE
For the Micro E-mini Nasdaq-100 on 10-minute timeframe:
Signal Sensitivity: 2.5-3.5
Score Amplifier: 50-70
Execution Mode: Adaptive
Min Bars Between: 3-5
Theme: Quantum Nebula or Dark Matter
💭 THE JOURNEY - FROM IMPOSSIBLE THEORY TO TRADING REALITY
Creating AHFT was a mathematical odyssey that pushed the boundaries of what's possible in Pine Script. The journey began with a seemingly impossible question: Could the profound mathematical structures of theoretical physics be translated into practical trading tools?
The Theoretical Challenge:
Months were spent diving deep into differential geometry textbooks, studying the works of Chern, Simons, and Witten. The mathematics of holonomy groups and gauge theory had never been applied to financial markets. Translating abstract mathematical concepts like parallel transport and fiber bundles into discrete price calculations required novel approaches and countless failed attempts.
The Computational Nightmare:
Pine Script wasn't designed for quantum field theory calculations. Implementing the Wilson loop integral, managing complex array structures for anomaly detection, and maintaining computational efficiency while calculating geometric phases pushed the language to its limits. There were moments when the entire project seemed impossible - the script would timeout, produce nonsensical results, or simply refuse to compile.
The Breakthrough Moments:
After countless sleepless nights and thousands of lines of code, breakthrough came through elegant simplifications. The realization that market anomalies follow patterns similar to quantum vacuum fluctuations led to the revolutionary anomaly detection system. The discovery that price paths exhibit holonomic memory unlocked the geometric phase calculations.
The Visual Revolution:
Creating visualizations that could represent 4-dimensional quantum fields on a 2D chart required innovative approaches. The multi-layer holonomy field, recursive quantum portals, and phase space flow representations went through dozens of iterations before achieving the perfect balance of beauty and functionality.
The Balancing Act:
Perhaps the greatest challenge was maintaining mathematical rigor while ensuring practical trading utility. Every formula had to be both theoretically sound and computationally efficient. Every visual had to be both aesthetically pleasing and information-rich.
The result is more than a strategy - it's a synthesis of pure mathematics and market reality that reveals the hidden order within apparent chaos.
📚 INTEGRATED DOCUMENTATION
Once applied to your chart, AHFT includes comprehensive tooltips on every input parameter. The source code contains detailed explanations of the mathematical theory, practical applications, and optimization guidelines. This published description provides the overview - the indicator itself is a complete educational resource.
⚠️ RISK DISCLAIMER
While AHFT employs advanced mathematical models derived from theoretical physics, markets remain inherently unpredictable. No mathematical model, regardless of sophistication, can guarantee future results. This strategy uses realistic commission ($0.62 per contract) and slippage (1 tick) in all calculations. Past performance does not guarantee future results. Always use appropriate risk management and never risk more than you can afford to lose.
🌟 CONCLUSION
The Anomalous Holonomy Field Theory represents a quantum leap in technical analysis - literally. By applying the profound insights of differential geometry, quantum field theory, and gauge theory to market analysis, AHFT reveals structure and opportunities invisible to traditional methods.
From the holonomy calculations that capture market memory to the anomaly detection that identifies phase transitions, from the adaptive intelligence that learns and evolves to the stunning visualizations that make the invisible visible, every component works in mathematical harmony.
This is more than a trading strategy. It's a new lens through which to view market reality.
Trade with the precision of physics. Trade with the power of mathematics. Trade with AHFT.
I hope this serves as a good replacement for Quantum Edge Pro - Adaptive AI until I'm able to fix it.
— Dskyz, Trade with insight. Trade with anticipation.
Donchian x WMA Crossover (2025 Only, Adjustable TP, Real OHLC)Short Description:
Long-only breakout system that goes long when the Donchian Low crosses up through a Weighted Moving Average, and closes when it crosses back down (with an optional take-profit), restricted to calendar year 2025. All signals use the instrument’s true OHLC data (even on Heikin-Ashi charts), start with 1 000 AUD of capital, and deploy 100 % equity per trade.
Ideal parameters configured for Temple & Webster on ASX 30 minute candles. Adjust parameter to suit however best to download candle interval data and have GPT test the pine script for optimum parameters for your trading symbol.
Detailed Description
1. Strategy Concept
This strategy captures trend-driven breakouts off the bottom of a Donchian channel. By combining the Donchian Low with a WMA filter, it aims to:
Enter when volatility compresses and price breaks above the recent Donchian Low while the longer‐term WMA confirms upward momentum.
Exit when price falls back below that same WMA (i.e. when the Donchian Low crosses back down through WMA), but only if the WMA itself has stopped rising.
Optional Take-Profit: you can specify a profit target in decimal form (e.g. 0.01 = 1 %).
2. Timeframe & Universe
In-sample period: only bars stamped between Jan 1 2025 00:00 UTC and Dec 31 2025 23:59 UTC are considered.
Any resolution (e.g. 30 m, 1 h, D, etc.) is supported—just set your preferred timeframe in the TradingView UI.
3. True-Price Execution
All indicator calculations (Donchian Low, WMA, crossover checks, take-profit) are sourced from the chart’s underlying OHLC via request.security(). This guarantees that:
You can view Heikin-Ashi or other styled candles, but your strategy will execute on the real OHLC bars.
Chart styling never suppresses or distorts your backtest results.
4. Position Sizing & Equity
Initial capital: 1 000 AUD
Size per trade: 100 % of available equity
No pyramiding: one open position at a time
5. Inputs (all exposed in the “Inputs” tab):
Input Default Description
Donchian Length 7 Number of bars to calculate the Donchian channel low
WMA Length 62 Period of the Weighted Moving Average filter
Take Profit (decimal) 0.01 Exit when price ≥ entry × (1 + take_profit_perc)
6. How It Works
Donchian Low: ta.lowest(low, DonchianLength) over the specified look-back.
WMA: ta.wma(close, WMALength) applied to true closes.
Entry: ta.crossover(DonchianLow, WMA) AND barTime ∈ 2025.
Exit:
Cross-down exit: ta.crossunder(DonchianLow, WMA) and WMA is not rising (i.e. momentum has stalled).
Take-profit exit: price ≥ entry × (1 + take_profit_perc).
Calendar exit: barTime falls outside 2025.
7. Usage Notes
After adding to your chart, open the Strategy Tester tab to review performance metrics, list of trades, equity curve, etc.
You can toggle your chart to Heikin-Ashi for visual clarity without affecting execution, thanks to the real-OHLC calls.
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version)
**Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory.
It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk.
⚠️ _For educational and research purposes only. Past performance does not guarantee future results._
---
## 🎯 Strategy Objective
- Smooth price noise using Kaufman Filter smoothing
- Detect the strength and direction of trends with a normalized oscillator
- Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic
---
## ✨ Key Features
- **Kaufman Filter Trend Detection**
Extracts directional signal using a state space model.
- **Multi-Stage Profit-Taking**
Automatically takes partial profits based on color changes and zero-cross events.
- **ATR-Based Volatility Stops**
Stops adjust based on swing highs/lows and current market volatility.
---
## 📊 Entry & Exit Logic
**Long Entry**
- `trend_strength ≥ 60`
- Green trend signal
- Price above the Kaufman average
**Short Entry**
- `trend_strength ≤ -60`
- Red trend signal
- Price below the Kaufman average
**Exit (Long/Short)**
- Blue trend color → TP1 (50%)
- Oscillator crosses 0 → TP2 (25%)
- Trend weakens → Final exit (25%)
- ATR + swing-based stop loss
---
## 💰 Risk Management
- Initial capital: `$3,000`
- Order size: `$100` per trade (realistic, low-risk sizing)
- Commission: `0.002%`
- Slippage: `2 ticks`
- Pyramiding: `1` max position
- Estimated risk/trade: `~0.1–0.5%` of equity
> ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._
---
## ⚙️ Default Parameters
- **1st Take Profit**: 50%
- **2nd Take Profit**: 25%
- **Final Exit**: 25%
- **ATR Period**: 14
- **Swing Lookback**: 10
- **Entry Threshold**: ±60
- **Exit Threshold**: ±40
---
## 📅 Backtest Summary
- **Symbol**: USD/JPY
- **Timeframe**: 1H
- **Date Range**: Jan 3, 2022 – Jun 4, 2025
- **Trades**: 924
- **Win Rate**: 41.67%
- **Profit Factor**: 1.108
- **Net Profit**: +$1,659.29 (+54.56%)
- **Max Drawdown**: -$1,419.73 (-31.87%)
---
## ✅ Summary
This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness.
It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users.
---
## 📌 Disclaimer
This script is for educational and informational purposes only and should not be considered financial advice.
Use with proper risk controls and always test in a demo environment before live trading.
Supply/Demand Zones + Engulfment-based ExecutionSupply/Demand Zones + Engulfment-Based Execution
Strategy Overview
This strategy combines institutional trading concepts—supply/demand zones and engulfing candle patterns—to generate high-probability long and short trade setups. The system uses aggregated price action to identify potential reversal zones and confirms entries with engulfing candle patterns, ensuring trades are only taken when market structure shows commitment in the direction of the trade.
Core Concepts
• Supply & Demand Zones: These are automatically detected by analyzing aggregated bullish and bearish candle structures over user-defined intervals. Supply zones are formed after bearish continuation patterns; demand zones appear after bullish continuation patterns.
• Engulfing Entries: Once price enters a zone, the strategy waits for a bullish engulfing pattern (in a demand zone) or a bearish engulfing pattern (in a supply zone) before executing a trade. This adds confirmation and reduces false signals.
• Risk Management: Stop-loss is placed at the low (for long trades) or high (for short trades) of the engulfed candle. Take-profit can be calculated using a fixed R-multiple (risk-to-reward ratio) or a user-defined target price.
Key Features
Fully customizable aggregation factor for zone detection
Visual zone boxes, entry/SL/TP boxes, and engulfing pattern labels
Optional removal of mitigated zones for cleaner charting
Configurable trade mode (Long only, Short only, or Both)
Support for trading sessions and date filtering
Alerts for price entering supply or demand zones
How to Use
Select Aggregation Factor: Choose how many candles to group together for identifying key zones (e.g., 4x timeframe).
Enable Zones: Turn on supply and/or demand zones as needed.
Set Execution Parameters:
– Choose R-multiple (e.g., 2:1 risk-reward)
– Or use a fixed take-profit price
Define Trade Time Window:
– Set the date and time ranges to restrict execution
– Use Start Hour and End Hour to limit trades to specific sessions (e.g., London/New York)
Run on Desired Timeframe: Typically used on 15m–4H charts, depending on your strategy and the asset’s volatility.
Ideal For
• Traders using Smart Money Concepts (SMC)
• Those who value high-confluence entries
• Intraday to swing traders looking for structure-based automation
⚠️ Important Notes
• The strategy requires engulfing confirmation within the zone to enter a position.
• This script does not repaint and executes trades on a bar close basis.
• Backtest results may vary based on session filters and aggregation factor.
© Attribution
This strategy was developed by The_Forex_Steward and is licensed under the Mozilla Public License 2.0.
You are free to use, modify, and distribute it under the terms of that license.
Grid TLong V1The “Grid TLong V1” strategy is based on the classic Grid strategy, but in the mode of buying and selling in favor of the trend and only on Long. This allows to take advantage of large uptrend movements to maximize profits in bull markets. For this reason, excessively sideways or bearish markets may not be very conducive to this strategy.
Like our Grid strategies in favor of the trend, you can enter and exit with the balance with controlled risk, as the distance between each grid functions as a natural and adaptable stop loss and take profit. What differentiates it from bidirectional strategies is that Short uses a minimum amount of follow-through, so that the percentage distance between the grids is maintained.
In this version of the script the entries and exits can be chosen at market or limit , and are based on the profit or loss of the current position, not on the percentage change in price.
The user may also notice that the strategy setup is risk-controlled, because it risks 5% on each trade, has a fairly standard commission and modest initial capital, all in order to protect the strategy user from unrealistic results.
As with all strategies, it is strongly recommended to optimize the parameters for the strategy to be effective for each asset and for each time frame.
Volatility Bias ModelVolatility Bias Model
Overview
Volatility Bias Model is a purely mathematical, non-indicator-based trading system that detects directional probability shifts during high volatility market phases. Rather than relying on classic tools like RSI or moving averages, this strategy uses raw price behavior and clustering logic to determine potential breakout direction based on recent market bias.
How It Works
Over a defined lookback window (default 10 bars), the strategy counts how many candles closed in the same direction (i.e., bullish or bearish).
Simultaneously, it calculates the price range during that window.
If volatility is above a minimum threshold and a clear directional bias is detected (e.g., >60% of closes are bullish), a trade is opened in the direction of that bias.
This approach assumes that when high volatility is coupled with directional closing consistency, the market is probabilistically more likely to continue in that direction.
ATR-based stop-loss and take-profit levels are applied, and trades auto-exit after 20 bars if targets are not hit.
Key Features
- 100% non-indicator-based logic
- Statistically-driven directional bias detection
- Works across all timeframes (1H, 4H, 1D)
- ATR-based risk management
- No pyramiding, slippage and commissions included
- Compatible with real-world backtesting conditions
Realism & Assumptions
To make this strategy more aligned with actual trading environments, it includes 0.05% commission per trade and a 1-point slippage on every entry and exit.
Additionally, position sizing is set at 10% of a $10,000 starting capital, and no pyramiding is allowed.
These assumptions help avoid unrealistic backtest results and make the performance metrics more representative of live conditions.
Parameter Explanation
Bias Window (10 bars): Number of past candles used to evaluate directional closings
Bias Threshold (0.60): Required ratio of same-direction candles to consider a bias valid
Minimum Range (1.5%): Ensures the market is volatile enough to avoid noise
ATR Length (14): Used to dynamically define stop-loss and target zones
Risk-Reward Ratio (2.0): Take-profit is set at twice the stop-loss distance
Max Holding Bars (20): Trades are closed automatically after 20 bars to prevent stagnation
Originality Note
Unlike common strategies based on oscillators or moving averages, this script is built on pure statistical inference. It models the market as a probabilistic process and identifies directional intent based on historical closing behavior, filtered by volatility. This makes it a non-linear, adaptive model grounded in real-world price structure — not traditional technical indicators.
Disclaimer
This strategy is for educational and experimental purposes only. It does not constitute financial advice. Always perform your own analysis and test thoroughly before applying with real capital.
Price Statistical Strategy-Z Score V 1.01
Price Statistical Strategy – Z Score V 1.01
Overview
A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”.
This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included.
Core Methodology
The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits.
Signal Conditions
Entry Condition:
A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met.
Exit Condition:
The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow.
Signal Gapping:
A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise.
Momentum Filter:
Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles.
Z-Score Function
The Z-Score is calculated as:
Z = (Close - SMA(Close, N)) / STDEV(Close, N)
Where N is the base period selected by the user.
Input Parameters
Enable Smoothed Z-Score Strategy
Enables or disables the Z-Score strategy logic. When disabled, no trades are executed.
Z-Score Base Period
Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes.
Short-Term Smoothing
Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility.
Long-Term Smoothing
Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic.
Bars gap between identical signals
Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals.
Trade Visualization Table
A table positioned at the bottom-right displays live PnL for open trades:
Entry Price
Unrealized PnL %
Text colors adapt based on whether unrealized profit is positive, negative, or neutral.
Technical Notes
This strategy uses only close prices — no trend indicators or volume components are applied.
All calculations are based on simple moving averages and standard deviation over user-defined windows.
Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
