암살자의 그리드 B 동적 그리드 거래 전략
저자:차오장, 날짜: 2024-02-20 10:47:48태그:
전반적인 설명
이 파인 스크립트는 자동 실행 및 백테스팅을 위한 strategy.orders( 기능을 사용하여 TradingView에서 그리드 거래 전략을 테스트하고 최적화하는 강력한 프레임워크를 제공합니다.
전략 논리
이 전략의 핵심 원칙은 진입 및 출구를 유도하는 가격 레벨의 계층을 포함하는 동적 그리드 시스템의 개념에 있습니다. 가격은 각 그리드 라인을 넘어서면 거래가 시작됩니다.
특히, 더 긴 포지션은 하락 추세에서 지지 부근에 입력됩니다. 수익은 상승 추세에서 주요 저항 수준에서 포지션을 평평화하여 캡처됩니다. 그리드 너비와 비율은 시장 변동성과 거래 프로필에 따라 조정됩니다.
이 자동화된 네트워크 메커니즘을 통해 전략은 양방향 가격 변동의 기회를 활용하여 일관된 긍정적 수익을 창출하는 것을 목표로합니다.
이점 분석
이 전략의 주요 장점은 다음과 같습니다.
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동적 격자: 시장 역동성에 맞게 사용자 정의 가능한 그리드 유형, 너비 설정 및 피프트 해상도.
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스마트 엔트리 규칙: 유연한 구매 옵션, 포지션 크기 구성 및 트렌드 기반의 구매량 통제.
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전술 출구: 포지션 사이징, 판매량 설정 및 손실 제한을 통해 최적화된 수익 접근 방식.
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다재다능한 설정: 다양한 거래 요구사항에 맞는 스팟 또는 마진 거래 방식.
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광범위한 구성성: 초기 자본, 수수료, 마진율 등과 같은 매개 변수를 사용하여 정밀하게 조정됩니다.
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정보 패널: 의사결정을 향상시키기 위해 중요한 데이터의 직관적인 시각화.
위험 분석
기능에도 불구하고 실제 세계 사용은 다음과 같은 것에 대한 인식을 요구합니다.
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백테스트 제한: 과거 결과는 미래의 성과를 정확하게 예측하지 못할 수 있습니다.
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시장 불확실성: 가격이 예상치 못한 변동으로 전략에 영향을 줄 수 있습니다.
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교환문제: 중개 시스템에서 오류가 발생하면 주문이 지연되거나 거절될 수 있습니다.
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시스템 장애: 주문 생성, 통신 또는 수신 과정의 장애.
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시간 지연: 실시간 거래 중 지연은 예상치 못한 결과를 초래할 수 있습니다.
이러한 본질적인 위험은 신중하고 측정된 실행을 가능하게 하기 위해 이 전략을 사용하여 실시간 자동화 거래에 참여하기 전에 평가되어야 합니다.
더 나은 기회
가능한 최적화는 다음과 같습니다.
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극단적인 움직임에서 손실을 제한하기 위해 스톱 로스 로직을 포함합니다.
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동적으로 그리드 설정을 캘리브레이트하기 위해 기계 학습을 구현합니다.
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트렌드 예측 및 주요 레벨을 위한 양적 지표를 통합하여 결정을 강화합니다.
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마진 콜 리스크를 방지하기 위한 리스크 통제를 도입합니다.
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거래 입출동의 효율적인 타이밍을 위해 시간 프레임에 걸쳐 분석합니다.
이러한 개선은 백테스트와 라이브 환경에서 더 높은 성능을 발휘할 수 있습니다.
결론
이 파인 스크립트는 그리드 방법론을 테스트하고 정비하는 강력한 기반을 제공합니다. 일부 실행 지연에도 불구하고 정보화된 위험 조정과 함께 신중한 응용은이 전략을 금융 시장에서 효과적인 자동화된 그리드 거래 시스템으로 사용할 수있게합니다.
/*backtest
start: 2023-02-19 00:00:00
end: 2024-02-01 05:20:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © AssassinsGrid
// Embark on an automated grid-based trading journey with this Pine script tailored for backtesting in TradingView using strategy.orders().
// Whether you're a seasoned trader or new to the world of financial markets, this script is designed to enhance your trading experience across various assets.
// It's essential to note that while powerful, this script may have a slightly delayed execution compared to the original Assassin's Grid due to the nature of strategy.orders() and strategy.close() functions,
// which execute on the following bar after the price crosses the grid.
//
// Key Features:
//
// 📊 Dynamic Grid Configuration: Define the grid type, width, and pivot point resolution to adapt to market dynamics.
//
// 📈 Smart Buy Strategies: Choose from various buy types, set quantities, and control the number of buy positions based on market trends.
//
// 💹 Strategic Selling: Optimize sell strategies with flexible options, including setting quantities, controlling sell positions, and defining loss thresholds.
//
// 🌐 Versatile Trading: Select between spot and margin trading modes, offering flexibility for diverse trading preferences.
//
// ⚙️ Detailed Configuration: Fine-tune your strategy with parameters like initial capital, commission values, margin rates, and more.
//
// 📊 Informative Chart Elements: Visualize critical information with adjustable labels, table size, grid visibility, and an insightful information panel.
//
// Conclusion:
//
// Whether you're a seasoned trader or a trading enthusiast exploring markets beyond cryptocurrencies, this Pine script provides a robust framework for testing and refining your strategies.
// While powerful, be aware that the execution may have a slight delay compared to the original Assassin's Grid.
// Dive into the dynamic world of grid-based trading, explore various configurations, and refine your approach across a spectrum of financial assets. Happy trading! 🚀💰
//@version=5
strategy("Assassin's Grid B", shorttitle = "AAGG📈", overlay = true, max_labels_count = 500, initial_capital = 10000, default_qty_value = 0, process_orders_on_close = true)
// INPUTS
// Grid parameters
WOption = input.string('Geometric (% Fall)', 'Width Type', ['Arithmetic (Price)', 'Geometric (% Sell)', 'Geometric (% Fall)'], group = "GRID", tooltip = "Select the type of width for the grid:\n\n- Arithmetic (Price): Fixed width in price units, maintaining constant separation on the chart.\n\n- Geometric (% Sell): Width based on a percentage of the sell price, dynamically adjusting grid lines. (Typically between 5-15%)\n\n- Geometric (% Fall): Width based on a percentage of potential market decline from the last sale, helping visualize potential drops. (Usually between 40-80%)")
Width = input.float(75, 'Width Parameter', minval = 0, step = 0.5, group = "GRID", tooltip = "Set the width parameter for the grid. Larger values result in wider grid lines.")
ppPeriod = input.string('4W', 'Pivot Point Resolution', group = "GRID", tooltip = "Set the resolution for the Pivot Point. Type a timeframe that suits your trading style: 'H, D, W, M'")
MARes = input.timeframe('D', 'EMA Resolution', group = "GRID", tooltip = "Set the resolution for the Exponential Moving Average (EMA). Choose a timeframe that suits your analysis.")
MALength = input.int(100, 'MA Length', minval = 1, group = "GRID", tooltip = "Set the length for the Moving Average (MA). This affects the responsiveness of the MA to price changes.")
// Buy
BuyType = input.string('Cash / n Buys', 'Buy Type', ['Contracts', 'Cash', '% Cash', 'Cash / n Buys', 'Cash / n Buys +'], group = "BUY", tooltip = "Select the type of buy: \n- Contracts: Specify the number of contracts to buy. \n- Cash: Specify the amount of cash to invest. \n- % Cash: Specify the percentage of available cash to invest. \n- Cash / n Buys: Distribute available cash equally across n buy positions. \n- Cash / n Buys +: Similar to Cash / n Buys, but the quantity on each buy increases proportionally with the buy number.")
BuyQ = input.float(10.0, 'Contracts / Cash / % Cash', minval = 0, group = "BUY", tooltip = "Set the quantity for buy. For 'Contracts' mode, it represents the number of contracts to buy. For 'Cash' and '% Cash' modes, it represents the amount to invest or the percentage of cash to invest, respectively.")
NBuysUp = input.int(4, 'N Buys over MA', minval = 1, maxval = 30, group = "BUY", tooltip = "Set the maximum number of buy positions allowed above the Moving Average (MA). This helps control the number of buys in an uptrend.")
NBuysDown = input.int(5, 'N Buys under MA (Max.)', minval = 1, maxval = 30, group = "BUY", tooltip = "Set the maximum number of buy positions allowed below the Moving Average (MA). This helps control the number of buys in a downtrend.")
LastXtrades = input.int(2, 'Buy all in last Trades', minval = 0, maxval = 10, group = "BUY", tooltip = "Set the number of the most recent buy trades to consider for making a buy decision. For the '% Cash' option only, this setting enables buying all available cash in the last specified number of trades.")
// Sel
SellType = input.string('Position / n Sells +', 'Sell Type', ['Contracts', 'Cash', '% Position', 'Position / n Sells', 'Position / n Sells +'], group = "SELL", tooltip = "Select the type of sell: \n- Contracts: Specify the number of contracts to sell. \n- Cash: Specify the amount of cash to disinvest. \n- % Position: Specify the percentage of the position to sell. \n- Position / n Sells: Distribute the position equally across n sell positions. \n- Position / n Sells +: Similar to Position / n Sells, but the quantity on each sell increases proportionally with the sell number.")
SellQ = input.float(5.0, 'Contracts / Cash / % Position', minval = 0, group = "SELL", tooltip = "Set the quantity for sell. For 'Contracts' mode, it represents the number of contracts to sell. For 'Cash' and '% Position' modes, it represents the amount to disinvest or the percentage of the position to sell, respectively.")
NSellsUp = input.int(20, 'N Sells over MA (Max.)', minval = 1, maxval = 30, group = "SELL", tooltip = "Set the maximum number of sell positions allowed above the Moving Average (MA). This helps control the number of sells in an uptrend.")
NSellsDown = input.int(4, 'N Sells under MA', minval = 1, maxval = 30, group = "SELL", tooltip = "Set the maximum number of sell positions allowed below the Moving Average (MA). This helps control the number of sells in a downtrend.")
LossAllowed = input.string('Never', 'Loss Allowed', ['Never', 'Last buy', 'Always'], group = "SELL", tooltip = "Set the loss allowed criteria: \n- Never: No loss allowed for selling. \n- Last buy: Allow selling if the current price is above the price of the last buy. \n- Always: Allow selling at any price.")
// Trading
TradingType = input.string('Spot', 'Trading Type', ['Spot', 'Margin'], group = "TRADING", tooltip = "Select the type of trading: \n- Spot: Regular trading without margin. \n- Margin: Trading with margin, allowing for leveraged positions.")
InitialContracts = input.float(10.0, '% Initial Capital 1st Trade', minval = 1, maxval = 100, group = "TRADING", tooltip = "Set the percentage of the initial capital to be used for the first trade. This determines the position size for the first trade.") / 100
CommissionValue = input.float(0.1, '% Commission Value', minval = 0, maxval = 100, step = 0.1, group = "TRADING", tooltip = "Set the percentage of the trade value to be considered as a commission. This is deducted from the trading capital.") / 100
MarginRate = input.float(1.0, '% Margin Rate', minval = 0, maxval = 100, step = 0.5, group = "TRADING", tooltip = "Set the percentage of margin rate to be applied. This is relevant only in margin trading scenarios.") / 100
StartDate = timestamp('01 Jan 1970 00:00 +000')
testPeriodStart = input(StartDate, 'Start of Trading', group = "TRADING", tooltip = "Set the start date for trading. The strategy will start evaluating trades from this date onwards.")
TableSizeInput = input.string('Small', 'Table Size', ['Auto', 'Tiny', 'Small', 'Normal', 'Large', 'Huge'], group = "PLOTTING", tooltip = "Set the size of the information table. Choose a size that suits your preference.")
ShowGrid = input(true, 'Level Grid', group = "PLOTTING", tooltip = "Show or hide the level grid on the chart. The grid represents price levels.")
ShowPanel = input(false, 'Information Panel', group = "PLOTTING", tooltip = "Show or hide the information panel on the chart. The panel displays key information about the strategy and current market conditions.")
ShowLiquidationPrice = input(false, 'Liquidation Price', group = "PLOTTING", tooltip = "Show or hide the liquidation price on the chart. The liquidation price is the level at which liquidation may occur, if applicable.")
// VARIABLES
// Grid levels on buys
var float _ldown = na
bool _pb = false
bool _buy = false
// Grid levels on sells
var float _lup = na
bool _ps = false
bool _sell = false
// First Buy
CloseFirstBar = ta.valuewhen(bar_index == 0, open, 0)
TimeFirstBar = ta.valuewhen(bar_index == 0, time, 0)
CloseStart = ta.valuewhen(time <= testPeriodStart, open, 0)
FirstClose = testPeriodStart > TimeFirstBar ? CloseStart : CloseFirstBar
TimeFirstClose = testPeriodStart > TimeFirstBar ? testPeriodStart : TimeFirstBar
// Buy and Sell prices
var float FinalBuyPrice = na
var float FinalSellPrice = na
var float FinalOpenPrice = na
var float BuyLimitPrice = na
var float SellLimitPrice = na
// Number of trades
var int nBuys = na
nBuys := nz(nBuys[1])
var int nSells = na
nSells := nz(nSells[1])
var int NBuys = NBuysDown
var int NSells = NSellsDown
// Quantities
var float BuyQuantity = 0
var float BuyAmount = 0
var float SellQuantity = 0
var float SellAmount = 0
var float Commission = 0
var float Gains = 0
var float Losses = 0
// Position calculation
var float PositionCash = 0
var float PositionSize = 0
var int BarIndex = 0
// Average Price Calculation
var float AvgPrice = 0
var float hl2Bar = 0
// Backtest information
var float Balance = 500000
var float Equity = 0
var float RealizedPnL = 0
var float PRealizedPnL = 0
var float Floating = 0
var float PFloating = 0
var float URealizedPnL = 0
var float PURealizedPnL = 0
var float Cash = Balance
var float Margin = 0
var float BuyAndHold = 0
var float PBuyAndHold = 0
var float CLeverage = 0
var float LiquidationPrice = 0
var bool Liquidation = false
var float ProfitFactor = 0
var int TradingTime = 0
// Fibonacci Pivots level calculation
var float PP = open
// Information panel
label labelBalance = na
// Analyzing when the period changes
bool PeriodChange = false
// Grid with arrays
aDown = array.new_float(30)
aUp = array.new_float(30)
aBuy = array.new_bool(30)
aSell = array.new_bool(30)
// Labels size
fTextSize(_SizeInput)=>
if _SizeInput == 'Auto'
size.auto
else if _SizeInput == 'Tiny'
size.tiny
else if _SizeInput == 'Small'
size.small
else if _SizeInput == 'Normal'
size.normal
else if _SizeInput == 'Normal'
size.normal
else if _SizeInput == 'Large'
size.large
else if _SizeInput == 'Huge'
size.huge
// Variable reference
var float MaxFinalOpenPrice = FirstClose
// Value of the MA
var float sMAValue = na
// GRID
// Function to calculate the Width
fWidth(_Width) =>
// If price is constant
if WOption == 'Arithmetic (Price)'
_Width
// If price is the Max % of the next Sell
else if WOption == 'Geometric (% Sell)'
MaxFinalOpenPrice * (_Width / 100)
// If price is a part of the % of the maximum fall
else if WOption == 'Geometric (% Fall)'
MaxFinalOpenPrice / NBuysDown * (_Width / 100)
// Origin from Rounded Pivot Points or last Sell
fDownGrid(_GridWidth) =>
if na(FinalOpenPrice)
FirstClose
else
if FinalSellPrice <= PP
if PositionSize > 0
if na(FinalBuyPrice)
if WOption == 'Arithmetic (Price)'
(math.floor(FinalSellPrice / _GridWidth) * _GridWidth) - _GridWidth
else
FinalSellPrice - _GridWidth
else
FinalBuyPrice - _GridWidth
else if PositionSize == 0
if WOption == 'Arithmetic (Price)'
(math.floor(PP / _GridWidth) * _GridWidth) - _GridWidth
else
PP
else
if na(FinalBuyPrice)
if WOption == 'Arithmetic (Price)'
(math.floor(PP / _GridWidth) * _GridWidth) - _GridWidth
else
if (FinalSellPrice - _GridWidth) > PP
PP
else
(FinalSellPrice - _GridWidth)
else
FinalBuyPrice - _GridWidth
// Origin for sells from Rounded Position Price
fUpGrid(_GridWidth) =>
if na(FinalSellPrice)
if LossAllowed == 'Never'
if WOption == 'Arithmetic (Price)'
math.ceil(math.max(AvgPrice, (FinalBuyPrice + _GridWidth)) / _GridWidth) * _GridWidth
else
math.max(AvgPrice, (FinalBuyPrice + _GridWidth))
else if LossAllowed == 'Last buy'
if nBuys == NBuys
FinalBuyPrice + _GridWidth
else
if WOption == 'Arithmetic (Price)'
math.ceil(math.max(AvgPrice, (FinalBuyPrice + _GridWidth)) / _GridWidth) * _GridWidth
else
math.max(AvgPrice, (FinalBuyPrice + _GridWidth))
else if LossAllowed == 'Always'
FinalBuyPrice + _GridWidth
else
FinalSellPrice + _GridWidth
// FUNCTIONS
// Function to sum factorial
fSum(_Num)=>
(math.pow(_Num, 2) + _Num) / 2
// Function when "Cash / n Buys" or "Position / n Sells"
fCaPo_N(_N, _n) =>
1 / (_N - nz(_n))
// Function when "Cash / n Buys +" or "Position / n Sells +"
fCaPo_Nplus(_OnSells, _N, _n) =>
if TradingType == 'Spot' or _OnSells == 1
(nz(_n)+1) / (fSum(_N) - fSum(nz(_n)))
else
(nz(_n)+1) / fSum(_N)
// One of the correct ways to use security
f_security(_sym, _res, _src, _rep) =>
request.security(_sym, _res, _src[not _rep and barstate.isrealtime ? 1 : 0])[_rep or barstate.isrealtime ? 0 : 1]
// Pivot points
PP := f_security(syminfo.tickerid, ppPeriod, hlc3, false)
// Moving Average
MA = ta.ema(close, MALength)
sMA = f_security(syminfo.tickerid, MARes, MA, false)
// Analyzing when the period changes
PeriodChange := ta.change(time(ppPeriod)) != 0
// On Bullish trend, less Number of Buys and more amounts per trade;
// on Bearish more Number of Buys and less amounts per trade
// Max. number of buys
NBuys := if (BuyType == "Cash / n Buys" or BuyType == "Cash / n Buys +")
if BuyLimitPrice >= sMAValue
NBuysUp
else
NBuysDown
else
NBuysDown
// On Bullish trend, more Number of Sells and less amounts per trade;
// on Bearish less Number of Sells and more amounts per trade
// Max. number of sells
NSells := if (SellType == "Position / n Sells" or SellType == "Position / n Sells +")
if SellLimitPrice < sMAValue
NSellsDown
else
NSellsUp
else
NSellsUp
// TRADING
// Start of trading
if time >= TimeFirstClose
// Final Trade Price, Average Price & Backtest
for _i = 1 to math.max(NBuys, NSells)
// Grid on Buys
array.insert(aDown, _i, fDownGrid(fWidth(Width)))
// Crossing between price and levels of grid
array.insert(aBuy, _i, (low <= array.get(aDown, _i) or open <= array.get(aDown, _i)) and nBuys <= NBuys-1)
// Grid on Sells
array.insert(aUp, _i, fUpGrid(fWidth(Width)))
// Crossing between price and levels of grid
array.insert(aSell, _i, (high >= array.get(aUp, _i) or open >= array.get(aUp, _i)) and nSells <= NSells-1)
strategy.initial_capital = 50000
// Financial Data
RealizedPnL := Balance - strategy.initial_capital
PRealizedPnL := (RealizedPnL / strategy.initial_capital) * 100
Floating := ((close - AvgPrice) / AvgPrice) * PositionSize * AvgPrice
PFloating := (Floating / Balance) * 100
URealizedPnL := RealizedPnL + Floating
PURealizedPnL := (URealizedPnL / strategy.initial_capital) * 100
Equity := Balance + Floating
Margin := TradingType == 'Spot' ? 0 : (PositionSize * AvgPrice * MarginRate)
Cash := TradingType == 'Spot' ? math.max(0, Balance - (PositionSize * AvgPrice)) : math.max(0, Balance - Margin)
BuyAndHold := ((close - FirstClose) / FirstClose) * strategy.initial_capital
PBuyAndHold := (BuyAndHold / strategy.initial_capital) * 100
CLeverage := (PositionSize * AvgPrice) / Balance
LiquidationPrice := TradingType == 'Spot' ? 0 : AvgPrice - ((Balance - (Margin * 1)) / PositionSize)
Liquidation := (ta.valuewhen(LiquidationPrice >= low, time , 0) <= timenow)
ProfitFactor := Gains / Losses
TradingTime := timenow - TimeFirstClose
// Quantities to buy according to inputs
if BuyType == "Contracts"
if na(FinalOpenPrice)
BuyQuantity := ((Cash * InitialContracts) / FirstClose)
else
BuyQuantity := math.min((Cash / AvgPrice), BuyQ)
else if BuyType == "Cash"
if na(FinalOpenPrice)
BuyQuantity := (Cash * InitialContracts)
else
BuyQuantity := math.min(Cash, BuyQ)
else if BuyType == "% Cash"
if na(FinalOpenPrice)
BuyQuantity := (Cash * InitialContracts)
else
if nBuys >= NBuys - LastXtrades
BuyQ := (1 / (NBuys - nz(nBuys))) * 100
BuyQuantity := math.min(Cash, (BuyQ / 100) * Cash)
else if BuyType == "Cash / n Buys"
if na(FinalOpenPrice)
BuyQuantity := (Cash * InitialContracts)
else
BuyQuantity := math.min(Cash, fCaPo_N(NBuys, nBuys) * Cash)
else if BuyType == "Cash / n Buys +"
if na(FinalOpenPrice)
BuyQuantity := (Cash * InitialContracts)
else
BuyQuantity := math.min(Cash, fCaPo_Nplus(0, NBuys, nBuys) * Cash)
// Quantities to sell according to inputs
if SellType == "Contracts"
SellQuantity := math.min(PositionSize, SellQ)
else if SellType == "Cash"
SellQuantity := math.min((PositionSize * AvgPrice), SellQ)
else if SellType == "% Position"
SellQuantity := math.min(PositionSize, (SellQ / 100) * PositionSize)
else if SellType == "Position / n Sells"
SellQuantity := math.min(PositionSize, fCaPo_N(NSells, nSells) * PositionSize)
else if SellType == "Position / n Sells +"
SellQuantity := math.min(PositionSize, fCaPo_Nplus(1, NSells, nSells) * PositionSize)
// First buy limit order from every change of the period
if (PP != PP[1]) and nz(nBuys) == 0 and not nz(nSells) == 0 and not na(nBuys) and not na(fDownGrid(fWidth(Width)))
// Value of the MA
sMAValue := sMA
// Buy price of the limit order
BuyLimitPrice := fDownGrid(fWidth(Width))
// Buying at better Price
if array.get(aBuy, _i) and BuyQuantity > 0
// Value of the MA
sMAValue := sMA
// Price of buy orders and resetting sales
FinalBuyPrice := math.min(open, array.get(aDown, _i))
FinalSellPrice := na
FinalOpenPrice := FinalBuyPrice
// Number of buys and resetting sales
nBuys += 1
nSells := na
// Redefining buy quantity
if BuyType == "Contracts"
BuyAmount := BuyQuantity
else
BuyAmount := (BuyQuantity / FinalBuyPrice)
// Calculating the priority and secondary price average
PositionCash += FinalBuyPrice * BuyAmount
PositionSize += BuyAmount
AvgPrice := PositionCash / PositionSize
// Calculating net profit
Balance -= (BuyAmount * FinalBuyPrice * CommissionValue)
// Comissions losses
Losses += (BuyAmount * FinalBuyPrice * CommissionValue)
// Fees paid
Commission += (BuyAmount * FinalBuyPrice * CommissionValue)
// Avoiding overlap
BarIndex := bar_index
// Variable reference
MaxFinalOpenPrice := math.max(FinalBuyPrice, nz(MaxFinalOpenPrice))
// Buy & Sell price of the limit orders
if nBuys <= NBuys-1
BuyLimitPrice := FinalBuyPrice - fWidth(Width)
else
BuyLimitPrice := na
SellLimitPrice := fUpGrid(fWidth(Width))
// Strategy orders
strategy.order("Buy", strategy.long, BuyAmount)
// Buy shapes
//string BuyText = str.tostring(BuyAmount,'#.####') + "\n" + str.tostring(((BuyAmount * FinalBuyPrice) / Cash) * 100, '#.##') + "%"
//c_BuyGrad = color.from_gradient(((NBuys - nBuys) / NBuys) * 100, 1, 100, color.lime, color.blue)
//label.new(bar_index, FinalBuyPrice, BuyText, textcolor = color.new(color.white, 50), color = c_BuyGrad, style = label.style_diamond, size = fTextSize(LabelSizeInput))
// Selling at better Price
else if array.get(aSell, _i) and SellQuantity > 0 and BarIndex != bar_index
// Value of the MA
sMAValue := sMA
// Price of sale orders and resetting buys
FinalBuyPrice := na
FinalSellPrice := math.max(open, array.get(aUp, _i))
FinalOpenPrice := FinalSellPrice
// Number of sales and resetting buys
nBuys := na
nSells += 1
// Redefining sell quantity
if SellType == "Cash"
SellAmount := SellQuantity / FinalSellPrice
else
SellAmount := SellQuantity
// Calculating the priority and resetting secondary price average
PositionCash -= AvgPrice * SellAmount
PositionSize -= SellAmount
// Calculating net profit
Balance += (FinalSellPrice - AvgPrice) * SellAmount
Balance -= (SellAmount * FinalSellPrice * CommissionValue)
// Gains and Losses
if FinalSellPrice >= AvgPrice
Gains += (FinalSellPrice - AvgPrice) * SellAmount
else
Losses += (AvgPrice - FinalSellPrice) * SellAmount
// Comission losses
Losses += (SellAmount * FinalSellPrice * CommissionValue)
// Fees paid
Commission += (SellAmount * FinalSellPrice * CommissionValue)
// Variable reference
MaxFinalOpenPrice := FinalSellPrice
// Buy & Sell price of the limit orders
BuyLimitPrice := fDownGrid(fWidth(Width))
if nSells <= NSells-1
SellLimitPrice := FinalSellPrice + fWidth(Width)
else
SellLimitPrice := na
// Strategy orders
strategy.close("Buy", comment = 'Sell', qty = SellAmount)
// Sell shapes
//string SellText = str.tostring(SellAmount,'#.####') + "\n" + str.tostring((SellAmount / (PositionSize + SellAmount)) * 100, '#.##') + "%"
//c_SellGrad = color.from_gradient(((NSells - nSells) / NSells) * 100, 1, 100, color.yellow, color.red)
//label.new(bar_index, FinalSellPrice, SellText, textcolor = color.new(color.white, 50), color = c_SellGrad, style = label.style_diamond, size = fTextSize(LabelSizeInput))
// PLOTTING
// Price of the limit orders
bool LastBar = (time >= timenow - (timeframe.multiplier * 1000 * 60))
plotshape(ShowGrid ? BuyLimitPrice : na, "Buy level", shape.cross, location.absolute, color.new(color.blue, 30), size = size.auto)
plotshape(ShowGrid ? SellLimitPrice : na, "Sell level", shape.cross, location.absolute, color.new(color.red, 30), size = size.auto)
// Table
var InfoPanel = table.new(position.middle_left, 2, 12, na, color.new(color.white, 80), 1, color.new(color.white, 80), 1)
ftable(_table_id, _column, _row, _text, _bgcolor) =>
table.cell(_table_id, _column, _row, _text, 0, 0, _bgcolor, text.align_left, text.align_center, fTextSize(TableSizeInput), na)
tfString(int timeInMs) =>
// @function Produces a string corresponding to the input time in days, hours, and minutes.
// @param (series int) A time value in milliseconds to be converted to a string variable.
// @returns (string) A string variable reflecting the amount of time from the input time.
float s = timeInMs / 1000
float m = s / 60
float h = m / 60
float d = h / 24
float mo = d / 30.416
int tm = math.floor(m % 60)
int th = math.floor(h % 24)
int td = math.floor(d % 30.416)
int tmo = math.floor(mo % 12)
int ys = math.floor(d / 365)
string result =
switch
d == 30 and th == 10 and tm == 30 => "1M"
d == 7 and th == 0 and tm == 0 => "1W"
=>
string yStr = ys ? str.tostring(ys) + "Y " : ""
string moStr = tmo ? str.tostring(tmo) + "M " : ""
string dStr = td ? str.tostring(td) + "D " : ""
string hStr = th ? str.tostring(th) + "H " : ""
string mStr = tm ? str.tostring(tm) + "min" : ""
yStr + moStr + dStr + hStr + mStr
if ShowPanel
ftable(InfoPanel, 0, 0, 'Equity: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 1, 'Position: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 2, 'Cash: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 3, 'Margin: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 4, 'Current Leverage: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 5, 'Commission Paid: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 6, 'Floating: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 7, 'Realized PnL: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 8, 'Unrealized PnL: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 9, 'Buy n Hold: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 10, 'Profit Factor: ' , color.new(color.white, 50))
ftable(InfoPanel, 0, 11, 'Time of Trading: ' , color.new(color.white, 50))
ftable(InfoPanel, 1, 0, Liquidation ? 'Liquidation' : str.tostring(Equity, '#.####') + ' ' + syminfo.currency , Liquidation ? color.red : color.green)
ftable(InfoPanel, 1, 1, str.tostring(PositionSize, '#.####') + ' ' + syminfo.basecurrency , color.green)
ftable(InfoPanel, 1, 2, str.tostring(Cash, '#.####') + ' ' + syminfo.currency , color.green)
ftable(InfoPanel, 1, 3, str.tostring(Margin, '#.####') + ' ' + syminfo.currency , color.green)
ftable(InfoPanel, 1, 4, TradingType == 'Spot' ? 'Spot' : str.tostring(CLeverage, '#.##') + 'x' , color.green)
ftable(InfoPanel, 1, 5, str.tostring(Commission, '#.####') + ' ' + syminfo.currency , Commission == 0 ? color.green : color.red)
ftable(InfoPanel, 1, 6, str.tostring(PFloating, '#.##') + ' %' , PFloating >= 0 ? color.green : color.red)
ftable(InfoPanel, 1, 7, str.tostring(PRealizedPnL, '#.##') + ' %' , PRealizedPnL >= 0 ? color.green : color.red)
ftable(InfoPanel, 1, 8, str.tostring(PURealizedPnL, '#.##') + ' %' , PURealizedPnL >= 0 ? color.green : color.red)
ftable(InfoPanel, 1, 9, str.tostring(PBuyAndHold, '#.##') + ' %' , PBuyAndHold >= 0 ? color.green : color.red)
ftable(InfoPanel, 1, 10, str.tostring(ProfitFactor, '#.##') , ProfitFactor >= 1 ? color.green : color.red)
ftable(InfoPanel, 1, 11, tfString(TradingTime) , color.new(color.white, 50))
// Plotting pivot points
plot(PP, title = "PP", style = plot.style_stepline, color = color.silver, linewidth = 1)
// Plotting the average price
plotshape(barstate.isrealtime and ta.change(AvgPrice) != 0 ? AvgPrice : na, "AvgPricepr", shape.diamond, location.absolute, color.new(color.yellow, 10), size = size.tiny)
plotshape( ta.change(AvgPrice[1]) != 0 ? AvgPrice[1] : na, "AvgPricep", shape.diamond, location.absolute, color.new(color.yellow, 40), size = size.tiny)
plotshape(TimeFirstClose == time ? FirstClose : na, "FirstClose", shape.diamond, location.absolute, color.new(color.yellow, 40), size = size.tiny)
// Plotting the moving average
plot((BuyType == "Cash / n Buys" or BuyType == "Cash / n Buys +") and NBuysUp != NBuysDown ? sMA : na, title = "Moving Average Buys", color = color.new(color.blue, 50), linewidth = 2)
plot((SellType == "Position / n Sells" or SellType == "Position / n Sells +") and NSellsUp != NSellsDown ? sMA : na, title = "Moving Average Sells", color = color.new(color.red, 50), linewidth = 2)
// Plotting the liquidation price
plot(ShowLiquidationPrice and LiquidationPrice > 0 ? LiquidationPrice : na, "Liquidation Price", Liquidation ? color.new(color.red, 30) : color.new(color.lime, 30), 2)
barcolor(Liquidation ? color.red : na)
//⚠️ IMPORTANT NOTICE: LIVE ALGORITHMIC TRADING RISKS
//🚨 Engaging in live algorithmic trading poses several inherent risks that you should be aware of:
//📉 Backtesting Limitations: Historical performance (backtesting) may not accurately predict future results. Past success does not guarantee future profits.
//🌐 Market Unpredictability: The market is dynamic and may fail or exhibit unexpected behavior, impacting your trading strategies.
//🛑 Broker Infrastructure Issues: Your broker's infrastructure may experience failures, execution delays, or reject your orders, affecting real-time trading.
//🤖 System Failures: The systems used for order generation, communication with your broker, and result reception might fail, disrupting the trading process.
//⏳ Time Lag Challenges: Time lags during live trading can lead to unexpected behavior and outcomes, requiring careful consideration.
//🤝 Third-Party System Risks: Systems of third parties, including service providers, brokers, and securities markets, may fail or malfunction, impacting overall functionality.
//💡 It's crucial to acknowledge and understand these risks before embarking on live algorithmic trading. Stay informed, be cautious, and adapt your strategies accordingly.
// Happy and safe trading! 🚀💰
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