YASINKARACAThe short-, medium-, and long-term indicator I have created is actually composed of moving averages, trend-following tools, Bollinger Bands, and various other indicators. You can use this indicator on any time frame.
If the turquoise moving average crosses above the orange moving average, it should be interpreted as a buy signal; if it crosses below, it should be interpreted as a sell signal.
Additionally, the white trend-following line indicates safety when the price is above it and risk when the price is below it.
The upper and lower lines of the Bollinger Bands are presented in gray. When the price approaches the lower Bollinger Band, it can be interpreted as a buying opportunity; when it reaches the upper band, it can be seen as a selling opportunity.
If the price is above the orange-colored slow moving average, the trend should be considered upward; if it is below, the trend is downward.
Wishing you success and a great day.
Yasin Karaca
지표 및 전략
Dskyz (DAFE) AI Adaptive Regime - Pro Dskyz (DAFE) AI Adaptive Regime - Pro: Redefining Trading Excellence
Introduction
welcome to a new era of trading! the Dskyz (DAFE) Ai Adaptive Regime - Pro is a strategy on TradingVew that combines ai-driven adaptability with practical trading tools. it’s built for traders of all levels—whether you’re just starting out or you’ve been in the game for years. this strategy dynamically adjusts to any market condition (trending, ranging, volatile, or quiet) using a unique blend of indicators and ai logic. it’s available on TradingView scripts and has been backtested on the nasdaq 100 micro futures (MNQ1!) to show real results. this is an updated version of my previous script , now with refined settings like a faster 3-period fast MA, a 2-minute HTF for RSI/MACD, and an exaggerated commission of $10 per side to demonstrate the strategy’s robustness.
What It Does and Why It’s Unique
this strategy isn’t just another tradingview script—it’s a system that thinks on its feet. most strategies struggle when market conditions change, but this one uses regime detection to identify the market’s state and adjust its approach. here’s the core idea: it analyzes the market using multiple indicators, assigns a “regime” (trending, ranging, volatile, or quiet), and then tailors its signals to match. this adaptability comes from combining several concepts:
regime detection: uses the average directional index (ADX) to spot trends (ADX > 15), bollinger bands (BB) width to measure volatility, and ATR to gauge price movement. this ensures the strategy knows when to trade aggressively (trending markets) or sit tight (ranging markets).
multi-timeframe confirmation: pulls RSI and MACD from a higher timeframe (default: 2 minutes) to filter out noise on the 1-minute chart. for example, if the 2-minute RSI is overbought (>75), it’ll avoid long trades even if the 1-minute chart looks bullish.
candlestick patterns: spots setups like bullish engulfing, hammers, and double bottoms, but only acts when volume confirms the move (volume > 1.5x the 20-period average). this cuts down on false signals.
dynamic risk management: sets stops and targets using ATR (default period: 3, multiplier: 4.6), so your risk adjusts to volatility. trailing stops kick in to lock in profits as the trade moves your way.
what makes this script stand out is how these pieces work together. the ADX and BBs define the market’s state, the HTF RSI/MACD confirms the trend, candlestick patterns pinpoint entries, and ATR keeps your risk in check. most TradingView scripts focus on one or two of these—like just following a moving average crossover—but this strategy’s multi-layered approach ensures it’s ready for any market. plus, it learns from recent trades (using a 5-trade window) to tweak its risk settings, making it smarter over time.
How It Works: Key Components
let’s break down the main indicators and how they fit into the strategy:
adaptive moving averages: uses a fast EMA (3-period) and slow EMA (36-period) to determine trend direction. if the fast MA is above the slow MA by more than 100x ATR, it’s a bullish trend. this helps avoid choppy markets where MAs often give false signals.
average true range (ATR): calculates dynamic stops and targets. for example, a long entry might set a stop at the recent swing low minus 4.6x ATR, ensuring the stop isn’t too tight in volatile markets.
multi-timeframe RSI & MACD: checks the 2-minute chart for RSI (overbought/oversold) and MACD (trend direction). this adds a layer of confirmation, so you’re not trading against the bigger trend.
bollinger bands: measures volatility to classify regimes. a wide BB (above the 50-period average) signals a volatile market, while a narrow BB suggests a quiet market. the bands also help spot overextended moves for potential reversals.
candlestick patterns: looks for patterns like hammers or double bottoms near support/resistance, but only if volume spikes to confirm. this ensures the pattern isn’t just noise.
these components feed into a scoring system. for a buy signal, the strategy checks the regime, trend direction, HTF indicators, and candlestick patterns, assigning points for each (e.g., a bullish engulfing pattern adds 0.5, a bullish HTF MACD adds 0.3). if the total score hits 1.0 or higher, it triggers a trade. this layered approach makes signals more reliable than a single-indicator strategy.
Default Properties and Backtest Settings
To keep things realistic and transparent, here are the default settings used in the script and
Backtest:
symbol: MNQ1! (nasdaq 100 micro futures)
timeframe: 1 minute
initial capital: $50,000 (realistic for the average trader)
commission: $10 per side ($20 round trip per trade), intentionally exaggerated to demonstrate the strategy’s effectiveness under high fees. typical micro futures commissions are $1.00-$3.00 round trip per contract, so this is ~10x higher to stress-test the system
slippage: 4 ticks (1 point, since MNQ has 4 ticks per point)
risk per trade: $200 (0.4% of capital, well below the 5-10% max risk guideline)
max contracts: 2 (but backtest shows max held is 1 contract per trade)
ATR period: 3
ATR multiplier: 4.6
fast MA: 3-period EMA
slow MA: 36-period EMA
HTF: 2 minutes for RSI/MACD
backtest period: march 23, 2025, 17:00 to april 17, 2025, 15:59 (25 days, 73 trades)
the backtest results showed a net profit of $10,573 with a 46.58% win rate and a 2.657 win/loss ratio over 73 trades. this averages 2.9 trades per day on a 1-minute chart. while this is below the recommended 100+ trades for a sample size, the strategy’s settings (e.g., fast MA of 3, slow MA of 36, 2-minute HTF) are tuned for fewer, higher-quality trades, and the stable win rate and strong profit factor (2.316) suggest reliability. the max drawdown was $1,394.50 (2.35% of initial capital), within the 5-10% guideline. total commission paid was $1,470 (73 trades * $20), which is high due to the exaggerated $10 per side fee. if using a more typical $1.00 per trade commission, total fees would be $73, increasing net profit to $11,050. this shows the strategy performs even better under realistic conditions.
How to Use It
this script is designed to be user-friendly, even if you don’t know pine script. here’s how to get started:
add to chart: load the script on tradingview and apply it to a 1-minute MNQ1! chart (or your preferred instrument/timeframe after testing).
check presets: the script offers three modes—aggressive, conservative, and optimized. start with “optimized” (default) for a balanced approach. you can switch modes in the inputs tab.
read the dashboard: a table at the bottom-left shows key metrics like trend direction, ATR, RSI, and the current regime (trending, ranging, etc.). use this to understand the market’s state.
watch for signals: green triangles below bars signal a buy, red triangles above bars signal a sell. the script also plots stop-loss and take-profit levels (red/green lines) for each trade.
monitor visuals: the chart background changes color based on the regime (green for trending, orange for ranging, etc.), and bollinger bands (upper green, lower red, center white) help you see volatility.
backtest first: before live trading, run a backtest with your settings to see how it performs on your instrument and timeframe. adjust risk per trade or max contracts in the inputs if needed.
for pros, you can dive into the inputs to tweak things like the ATR multiplier, MA lengths, or HTF timeframe. the script also includes a debug table (top-left) to show regime counts and indicator values, which is great for fine-tuning.
Key Features and Benefits
market adaptability: switches between four regimes to trade smarter in any condition.
precision signals: combines HTF RSI/MACD, candlestick patterns, and volume for reliable entries/exits.
smart risk management: ATR-based stops and trailing stops keep your capital safe.
self-optimizing ai: adjusts risk based on recent trades to improve over time.
user-friendly design: presets and a sleek dashboard make it easy for beginners.
visual appeal: color-coded backgrounds, glow-effect bands, and clear signal markers.
Limitations and Considerations
no strategy is perfect, so here’s what to keep in mind:
market dependency: works best on futures like MNQ or ES due to consistent tick values. stocks or forex may need adjustments to ATR or risk settings.
l earning curve: presets make it easy, but understanding regimes and indicators unlocks its full potential. check the dashboard to learn as you go.
no guarantees: success depends on market conditions and discipline. backtest thoroughly before live trading.
exaggerated commission: the backtest uses a high $10 per side commission to stress-test the strategy. real-world fees are typically lower, which would improve performance.
Conclusion
The Dskyz (DAFE) Ai adaptive regime - Pro is here to help you trade smarter, not harder. its ai-driven adaptability, multi-timeframe precision, and user-friendly design make it a powerful tool for any trader. despite a high $10 per side commission in the backtest, it still delivered a $10,573 profit, proving its robustness. under typical fees, performance would be even stronger. whether you’re chasing trends on MNQ or testing it on another instrument, this strategy gives you an edge with its dynamic signals and risk management. add it to your chart, backtest it, and see why it’s redefining trading excellence. let’s trade with discipline, vision, and pro-level precision! 🚀
—Dskyz
Prop Firm Business SimulatorThe prop firm business simulator is exactly what it sounds like. It's a plug and play tool to test out any tradingview strategy and simulate hypothetical performance on CFD Prop Firms.
Now what is a modern day CFD Prop Firm?
These companies sell simulated trading challenges for a challenge fee. If you complete the challenge you get access to simulated capital and you get a portion of the profits you make on those accounts payed out.
I've included some popular firms in the code as presets so it's easy to simulate them. Take into account that this info will likely be out of date soon as these prices and challenge conditions change.
Also, this tool will never be able to 100% simulate prop firm conditions and all their rules. All I aim to do with this tool is provide estimations.
Now why is this tool helpful?
Most traders on here want to turn their passion into their full-time career, prop firms have lately been the buzz in the trading community and market themselves as a faster way to reach that goal.
While this all sounds great on paper, it is sometimes hard to estimate how much money you will have to burn on challenge fees and set realistic monthly payout expectations for yourself and your trading. This is where this tool comes in.
I've specifically developed this for traders that want to treat prop firms as a business. And as a business you want to know your monthly costs and income depending on the trading strategy and prop firm challenge you are using.
How to use this tool
It's quite simple you remove the top part of the script and replace it with your own strategy. Make sure it's written in same version of pinescript before you do that.
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
//--$$$$$--Strategy-- --$$$$$$--// ******************************************************************************************************************************
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
length = input.int(20, minval=1, group="Keltner Channel Breakout")
mult = input(2.0, "Multiplier", group="Keltner Channel Breakout")
src = input(close, title="Source", group="Keltner Channel Breakout")
exp = input(true, "Use Exponential MA", display = display.data_window, group="Keltner Channel Breakout")
BandsStyle = input.string("Average True Range", options = , title="Bands Style", display = display.data_window, group="Keltner Channel Breakout")
atrlength = input(10, "ATR Length", display = display.data_window, group="Keltner Channel Breakout")
esma(source, length)=>
s = ta.sma(source, length)
e = ta.ema(source, length)
exp ? e : s
ma = esma(src, length)
rangema = BandsStyle == "True Range" ? ta.tr(true) : BandsStyle == "Average True Range" ? ta.atr(atrlength) : ta.rma(high - low, length)
upper = ma + rangema * mult
lower = ma - rangema * mult
//--Graphical Display--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
u = plot(upper, color=#2962FF, title="Upper", force_overlay=true)
plot(ma, color=#2962FF, title="Basis", force_overlay=true)
l = plot(lower, color=#2962FF, title="Lower", force_overlay=true)
fill(u, l, color=color.rgb(33, 150, 243, 95), title="Background")
//--Risk Management--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
riskPerTradePerc = input.float(1, title="Risk per trade (%)", group="Keltner Channel Breakout")
le = high>upper ? false : true
se = lowlower
strategy.entry('PivRevLE', strategy.long, comment = 'PivRevLE', stop = upper, qty=riskToLots)
if se and upper>lower
strategy.entry('PivRevSE', strategy.short, comment = 'PivRevSE', stop = lower, qty=riskToLots)
The tool will then use the strategy equity of your own strategy and use this to simulat prop firms. Since these CFD prop firms work with different phases and payouts the indicator will simulate the gains until target or max drawdown / daily drawdown limit gets reached. If it reaches target it will go to the next phase and keep on doing that until it fails a challenge.
If in one of the phases there is a reward for completing, like a payout, refund, extra it will add this to the gains.
If you fail the challenge by reaching max drawdown or daily drawdown limit it will substract the challenge fee from the gains.
These gains are then visualised in the calendar so you can get an idea of yearly / monthly gains of the backtest. Remember, it is just a backtest so no guarantees of future income.
The bottom pane (non-overlay) is visualising the performance of the backtest during the phases. This way u can check if it is realistic. For instance if it only takes 1 bar on chart to reach target you are probably risking more than the firm wants you to risk. Also, it becomes much less clear if daily drawdown got hit in those high risk strategies, the results will be less accurate.
The daily drawdown limit get's reset every time there is a new dayofweek on chart.
If you set your prop firm preset setting to "'custom" the settings below that are applied as your prop firm settings. Otherwise it will use one of the template by default it's FTMO 100K.
The strategy I'm using as an example in this script is a simple Keltner Channel breakout strategy. I'm using a 0.05% commission per trade as that is what I found most common on crypto exchanges and it's close to the commissions+spread you get on a cfd prop firm. I'm targeting a 1% risk per trade in the backtest to try and stay within prop firm boundaries of max 1% risk per trade.
Lastly, the original yearly and monthly performance table was developed by Quantnomad and I've build ontop of that code. Here's a link to the original publication:
That's everything for now, hope this indicator helps people visualise the potential of prop firms better or to understand that they are not a good fit for their current financial situation.
Scalping Strategy with DCA - V3# Enhanced Scalping Strategy with DCA - V3
## Strategy Overview
This strategy combines multiple technical indicators with a structured Dollar Cost Averaging (DCA) approach to create a comprehensive trading system for cryptocurrency markets. Unlike simple indicator mashups, this strategy integrates several confirmation layers for entries while implementing a sophisticated risk management system based on the 1-2-6 DCA ratio.
## What Makes This Strategy Unique
1. **Multi-Layered Entry Confirmation System**:
- Uses EMA crossover as the primary trigger
- Adds RSI momentum confirmation
- Integrates MACD for trend strength
- Includes RSI divergence for reversal potential
- Incorporates higher timeframe confirmation for trend alignment
2. **Structured Risk Management**:
- Implements a 1-2-6 DCA ratio to strategically average into positions
- Uses percentage-based stop losses that adapt based on DCA status
- Features a two-tiered take profit system (25% at TP1, 50% at TP2)
- Optional breakeven stop loss after second take profit target
- Initial risk limited to a small percentage of account (1-3%)
3. **Versatile Market Adaption**:
- Additional entry opportunities during oversold/overbought Bollinger Band touches
- Customizable filters that can be enabled/disabled based on market conditions
- Higher timeframe confirmation to ensure alignment with larger trends
## How The Components Work Together
### Entry System Components
1. **48 EMA** serves as the primary trend filter and entry trigger. Price crossing above/below this EMA signals a potential trend change.
2. **RSI (Relative Strength Index)** confirms momentum in the intended direction:
- For longs: RSI > 20 shows bullish momentum
- For shorts: RSI < 80 shows bearish momentum
3. **MACD (Moving Average Convergence Divergence)** filters out weak trends:
- For longs: MACD line crosses above signal line
- For shorts: MACD line crosses below signal line
4. **RSI Divergence Detection** identifies potential reversals where price makes a new high/low but RSI fails to confirm, suggesting momentum is weakening.
5. **Higher Timeframe Confirmation** ensures the trade aligns with the larger trend structure by checking EMA and RSI on a higher timeframe (default is daily).
6. **Bollinger Bands** provide additional entry triggers during strong oversold/overbought conditions:
- Long entry when price touches lower band with RSI < 20
- Short entry when price touches upper band with RSI > 80
### The DCA Mechanism
The strategy employs a 1-2-6 ratio for Dollar Cost Averaging:
- **Initial position**: 1 unit based on account risk percentage
- **First DCA level**: Adds 2 units when price moves against initial entry by the first DCA level percentage (default 1%)
- **Second DCA level**: Adds 6 units when price moves further against entry by the second DCA level percentage (default 2%)
This structured approach reduces average entry price during temporary adverse price movements, potentially converting losing trades into winners when the expected price movement eventually occurs.
### Exit Strategy
The strategy uses multiple exit mechanisms:
1. **Tiered Take Profits**:
- First TP at takeProfitPercent1 from entry (default 0.5%) - closes 25% of position
- Second TP at takeProfitPercent2 from entry (default 1.0%) - closes 50% of position
- Remaining 25% runs with trailing stop loss or until stopped out
2. **Stop Loss Management**:
- Initial SL set at stopLossPercent from entry (default 1.5%)
- After full DCA deployment, SL adjusts to fixedSLPercent from entry (default 1.3%)
- Optional breakeven SL after second take profit hits
## Backtesting Settings & Recommendations
For realistic backtesting, please configure the following in the strategy Properties panel:
- **Commission**: 0.075% (typical for major cryptocurrency exchanges)
- **Slippage**: 0.05% (accounts for execution delays and spread)
- **Initial Capital**: $10,000 (realistic starting capital for the average trader)
- **Date Range**: January 2024 to present (provides sufficient sample size)
These settings ensure backtesting results closely match real trading conditions. The strategy is designed to never risk more than 3% of account equity on any trade, with typical risk between 1-2%.
## Recommended Markets & Timeframes
This strategy performs best in:
1. **Markets**:
- Cryptocurrency markets with high liquidity
- Assets with market capitalization > $1 billion
- Coins with holder ratio > 7% (reducing manipulation risk)
2. **Timeframes**:
- Primary: 1-hour and 4-hour charts
- Secondary: 15-minute charts for faster execution
- Higher timeframe confirmation: Daily chart
## Parameter Customization Guide
The strategy offers multiple customization options to adapt to different trading styles and market conditions:
1. **Risk Settings**:
- initialRiskPercent: Adjust between 0.5-2% for conservative to moderate approaches
- stopLossPercent: 1-3% based on volatility of the asset
- takeProfitPercent1/2: Can be adjusted based on average volatility
2. **Entry Filters**:
- Enable/disable MACD filter for additional confirmation
- Enable/disable RSI divergence for reversal trading
- Enable/disable higher timeframe confirmation for trend alignment
3. **DCA Settings**:
- dcaLevel1/2: Adjust based on asset volatility (higher for more volatile assets)
- Change the 1-2-6 ratio by modifying the position size calculations
## Visual Outputs Explained
The strategy displays the following visual elements:
1. **Indicator plots**:
- 48 EMA (blue line): Main trend filter
- Bollinger Bands (upper: red, middle: yellow, lower: green): Volatility and overbought/oversold levels
2. **Trade management levels**:
- Stop Loss level (red circles): Current SL price
- Take Profit levels (green circles): TP1 and TP2 targets
3. **Information panel**:
- Displays strategy settings and current mode
- Shows active filters and risk parameters
- Reminds about market cap and holder ratio requirements
## Real-World Trading Tips
When implementing this strategy in real trading:
1. Start with conservative risk settings (0.5-1%)
2. Trade only in favorable market conditions initially
3. Consider reducing position size during high market uncertainty
4. Monitor higher timeframe trends before taking entries
5. Always check market cap and holder ratio before trading a coin
6. Set up proper alerts for EMA crosses with RSI confirmation
7. Regularly review and adjust parameters based on recent performance
## How to Use This Strategy
1. Add the strategy to your chart
2. Configure risk parameters appropriate for your account
3. Set commission and slippage in the Properties panel
4. Enable/disable the filters based on your trading style
5. Monitor higher timeframe for overall trend direction
6. Use the strategy's signals for entry and the recommended take profit/stop loss levels
7. Consider manual intervention during extreme market events
This strategy provides a systematic approach to scalping with proper risk management through DCA, making it suitable for both beginner and experienced traders in cryptocurrency markets.
SteveStrat 15MThis report provides a professional analysis of the BTC/USD pair on the 5-minute timeframe, focusing on an "Up and Down" trend assessment using Exponential Moving Averages (EMAs) and supplementary technical indicators. The goal is to identify the current trend and predict its future development based on real-time price data and market signals.
200/800 EMA Retest StrategyWe don't chase every EMA cross. We wait for the market to prove it's ready by pulling back to the 200 EMA — and then we strike, with smart risk control.
网格交易v1.0### Strategy Function Overview
**Grid Trading Framework:**
- Set up 11 grid buy levels evenly distributed between `maxPrice` and `minPrice`.
- Fund allocation increases progressively with each lower grid level (weights 1–11), resulting in more buying at lower prices.
- The grid range is controlled by the `range1` parameter (default: -10%).
**Semi-Final and Final DCA (Dollar-Cost Averaging) Buys:**
- A larger buy order is triggered when the price drops to the `semifinal` level (default: -12%).
- The largest buy order is triggered when the price drops to the `final` level (default: -15%).
**Take-Profit Mechanism:**
- **Primary Take-Profit:** All holdings are sold for profit when the average entry price rises by `tp%`.
- **First Order Trailing Take-Profit:** If only the first order has been executed, a dynamic trailing take-profit is set based on ATR.
**Visualization Elements:**
- **Grid Lines and Labels:** Show the price and quantity at each buy level.
- **Price Lines:** Indicate the highest grid price, the current average entry price, and the take-profit target.
- **Event Markers:** Flag for start time, 🛑 for semi-final trigger, and 🚀 for final trigger.
Enhanced VWAP Breakout StrategyBreakout. Retest. Confirm. Trade.
What This Strategy Does
This script tracks and trades VWAP breakouts, but adds a layer of discipline through retest confirmation logic.
Instead of entering the moment price breaks VWAP, it:
Waits for a breakout candle (close above VWAP for longs, below for shorts)
Monitors the retest, the next candle must dip into VWAP but hold its direction
Enters only on confirmation, a clean close above (or below) that retest high/low
Calculates your stop loss from the retest low/high
Plots target profit using your selected risk:reward ratio
Optionally manages trades with trailing stops
Everything is visualized:
Breakout candles = 🟡
Retest candles = 🟠
Entry triggers = 🟢
SL/TP levels plotted automatically
VWAP line adjustable in width and color
It keeps track of every step of the setup using arrays, so you’ll always know what phase the pattern is in, and where your trade came from.
How to Use It
Apply it to any timeframe (1h or 15m tends to work well with decent liquidity)
VWAP is built-in — no need to add a separate indicator
Configure your risk:reward in the settings (default = 3:1)
Use the session control to limit trades to specific hours or days (default = last 6 months)
Decide whether to enable shorts, or keep it long-only
Optionally enable trailing stops if you're managing runners
Once configured, the strategy will:
Highlight each phase of the pattern
Execute entries and exits for you
Track and display performance via TradingView’s built-in strategy tester
Perfect for those who want structure in their intraday or swing trades.
Why I Built It
I’ve seen too many breakouts fail because the entry was too early.
This strategy doesn’t jump the gun — it waits for the market to show its hand.
VWAP gives the bias, the retest gives the validation, and the trigger confirms momentum.
You can test it across markets, pairs, or even layer it into your own logic.
Try it out and share your thoughts. Feedback helps refine this even more.
More upgrades coming soon!
MACD + VWAP Strategy (6–9 PM & 7–8:30 AM EST)//@version=5
strategy("MACD + VWAP Strategy (6–9 PM & 7–8:30 AM EST)", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)
// === Inputs ===
macdFast = input.int(12, title="MACD Fast Length")
macdSlow = input.int(26, title="MACD Slow Length")
macdSignal = input.int(9, title="MACD Signal Length")
stopLossPts = input.float(10, title="Stop Loss (pts)")
takeProfitPts = input.float(25, title="Take Profit (pts)")
// === Indicators ===
= ta.macd(close, macdFast, macdSlow, macdSignal)
vwapLine = ta.vwap(close)
atrVal = ta.atr(14)
// === Time Filter ===
t = time("America/New_York")
hourEST = hour(t)
minuteEST = minute(t)
eveningSession = hourEST >= 18 and hourEST < 21 // 6:00 PM to 9:00 PM
morningSession = (hourEST == 7) or (hourEST == 8 and minuteEST < 30) // 7:00–8:30 AM
inSession = eveningSession or morningSession
// === Entry Conditions with Session Filter ===
longCondition = ta.crossover(macdLine, signalLine) and close > vwapLine and atrVal > 1.2 and inSession
shortCondition = ta.crossunder(macdLine, signalLine) and close < vwapLine and atrVal > 1.2 and inSession
if (longCondition)
strategy.entry("Long", strategy.long)
if (shortCondition)
strategy.entry("Short", strategy.short)
// === Exit Conditions ===
exitLong = ta.crossunder(macdLine, signalLine)
exitShort = ta.crossover(macdLine, signalLine)
if (exitLong)
strategy.close("Long")
if (exitShort)
strategy.close("Short")
// === Risk Management ===
strategy.exit("TP/SL Long", from_entry="Long", limit=close + takeProfitPts, stop=close - stopLossPts)
strategy.exit("TP/SL Short", from_entry="Short", limit=close - takeProfitPts, stop=close + stopLossPts)
// === Plotting ===
plot(vwapLine, title="VWAP", color=color.orange, linewidth=2)
// === Visuals ===
plotshape(longCondition, title="Long Entry", location=location.belowbar, color=color.green, style=shape.arrowup, size=size.small)
plotshape(shortCondition, title="Short Entry", location=location.abovebar, color=color.red, style=shape.arrowdown, size=size.small)
bgcolor(strategy.position_size > 0 ? color.new(color.green, 85) : na)
bgcolor(strategy.position_size < 0 ? color.new(color.red, 85) : na)
VWAP and MA Mean Reversion Strategy with ATR Stop LossVWAP and MA Mean reversion Strategy with ATR Stop losss_J
Jim Simons Inspired Quantitative StrategyThis sophisticated indicator implements quantitative trading methodologies inspired by Jim Simons' revolutionary theories and approaches. Rather than simply signaling standard buy/sell points, this advanced tool provides deeper trend analysis through a color-coded system:
Green signals indicate strong upward momentum
Red signals represent confirmed downward trends
Neutral coloration suggests potential trend reversals or transition points
The indicator excels on higher timeframes where market patterns become more pronounced, though it demonstrates remarkable versatility across all timeframes. By focusing on mathematical relationships rather than conventional technical indicators, this tool helps traders identify high-probability setups while filtering market noise.
Perfect for traders seeking to incorporate quantitative analysis into their strategy without requiring advanced mathematical expertise.
EMA Crossover Strategy with Trailing Stop and AlertsPowerful EMA Crossover Strategy with Dynamic Trailing Stop and Real-Time Alerts
This strategy combines the simplicity and effectiveness of EMA crossovers with a dynamic trailing stop-loss mechanism for robust risk management.
**Key Features:**
* **EMA Crossover Signals:** Identifies potential trend changes using customizable short and long period Exponential Moving Averages.
* **Trailing Stop-Loss:** Automatically adjusts the stop-loss level as the price moves favorably, helping to protect profits and limit downside risk. The trailing stop percentage is fully adjustable.
* **Visual Buy/Sell Signals:** Clear buy (green upward label) and sell (red downward label) signals are plotted directly on the price chart.
* **Customizable Inputs:** Easily adjust the lengths of the short and long EMAs, as well as the trailing stop percentage, to optimize the strategy for different assets and timeframes.
* **Real-Time Alerts:** Receive instant alerts for buy and sell signals, ensuring you don't miss potential trading opportunities.
**How to Use:**
1. Add the strategy to your TradingView chart.
2. Customize the "Short EMA Length," "Long EMA Length," and "Trailing Stop Percentage" in the strategy's settings.
3. Enable alerts in TradingView to receive notifications when buy or sell signals are generated.
This strategy is intended to provide automated trading signals based on EMA crossovers with built-in risk management. Remember to backtest thoroughly on your chosen instruments and timeframes before using it for live trading.
#EMA
#Crossover
#TrailingStop
#Strategy
#TradingView
#TechnicalAnalysis
#Alerts
#TradingStrategy
Enhanced Futures Multi-Signal StrategyThis is my second strategy indicator, please try to backtest and use it, hopefully it will be useful.
Phoenix Pro Strategy with AlertsPhoenix Pro Strategy with Alerts – Documentation
Version: Pine Script v5
Overlay: true
Purpose: Trend-based buy/sell strategy with alert support
🔧 Inputs
EMA Fast/Slow: Determines trend direction (default: 9, 21)
SMA Short/Long: Used for additional filtering (default: 20, 50)
RSI: Detects overbought/oversold levels (14 period)
Bollinger Bands: Measures price volatility and deviation from average (default: 20 period, 2.0 multiplier)
Pivot High/Low: Identifies support/resistance points (default: 5 bars left/right)
ZigZag: Highlights price swings based on percentage deviation (5%)
📈 Indicators & Calculations
EMA and SMA: Filters for trend direction and price positioning
RSI: Measures momentum
Bollinger Bands: Evaluates market volatility
Pivots & ZigZag: Visual support/resistance and trend reversals
✅ Long Entry Conditions
EMA Fast > EMA Slow
Price > SMA Short
RSI < 60
Price > Bollinger Basis
❌ Short Entry Conditions
EMA Fast < EMA Slow
Price < SMA Short
RSI > 40
Price < Bollinger Basis
Estrategia EMAs + RSI + MACD con SL y TPestrategia para enlazar con bitget. en pruebas para saber si es rentable
Trend-Following Strategy with Keltner ChannelsTrend Filter ( All Timeframes):
Price must be above the 200 EMA for a bullish setup
Price must be below the 200 EMA for a bearish setup
Keltner Channels for dynamic support/resistance
Buy when price pulls back to the midline or lower channel in an uptrend
Sell when price pulls back to the midline or upper channel in a downtrend
Entry trigger: Price pulls back to Keltner midline then crosses it
Exits: ATR-based SL & TP with optional trailing stops
Estrategia EMAs + RSI + MACD con SL y TPestrategia diseñada por Miguelito para btc en 4 horas jugando con rsi macd y 2 emas
Estrategia EMAs + RSI + MACD con SL y TPestrategia con emas rei macd y sl y tp para conseguir exprimir el grafico. en pruebas
Volume Range Profile with Fair Value (SWAROOPRAM29)This indicator overlays a custom volume profile across two dynamic price zones (upper and lower) within a defined lookback range. It highlights the Point of Control (POC) for each zone — the price level with the highest traded volume — and tracks its movement over time to help identify fair value areas.
🔍 Features:
Dynamic Volume Zones: Automatically calculates upper/lower zones based on a % of the high-low range.
Granular Volume Distribution: Visualizes buy/sell volume using histogram bins inside each zone.
Point of Control (POC): Shows the most active volume bin with optional percentage breakdown (Buy %, Sell %, Total).
Fair Value Drift Line: Plots a moving average of POC to track fair value shifts.
Total Volume Labels: Optionally display total buy/sell volume in each zone.
Trade Entry Logic (Strategy):
Entry on POC touch or midline level.
Supports long, short, or both directions.
Customizable take profit and stop loss levels.
This tool helps traders spot high-volume price acceptance areas, potential reversals, and trend continuations based on volume-weighted logic — ideal for scalping, swing, and volume-based strategies.
brings Smart Money Concept (ICT) session logic to life by plotting key global trading sessions with volume and delta analytics. It not only highlights session ranges but also tracks their midpoints — which often act as intraday support/resistance levels.
KEY FEATURES
Visual session boxes: Plots boxes for Tokyo, London, New York, and Sydney sessions based on user-defined UTC+0 time ranges.
Volume & delta metrics: Displays total volume and delta volume (buy–sell difference) within each session.
Mid, High & Low Range Extension: Once a session ends, the high, low, and midpoint levels automatically extend — ideal for detecting SR zones.
Session labels: Each box includes a label with session name, time, volume, and delta for quick reference.
Custom session control: Enable or disable sessions individually and configure start/end times.
Clean aesthetics: Transparent shaded boxes with subtle borders make it easy to overlay without clutter.
Sessions Dashboard: Shows the time range of each session and tells you whether the session is currently active.
🔵 USAGE
Enable the sessions you want to monitor (e.g., New York or Tokyo) from the settings.
Use session volume and delta values to gauge the strength and direction of institutional activity.
Watch for price interaction with the extended range — it often acts as dynamic support/resistance after the session ends.
Overlay it with liquidity tools or breaker blocks for intraday strategy alignment.
🔵 EXAMPLES
Extended Future Range acted as resistance/support.
Vinicius Setup ATR PRO🧠 Vinicius Setup ATR PRO – Automated Bot with Supertrend + RSI + Volume
This is a professional and fully automated trading setup, built to trade BTC/USDT on the 1-hour chart, using a solid logic based on:
✅ Supertrend (with ATR) as the main directional indicator
✅ Strong candlestick + above-average volume as entry triggers
✅ RSI filter to avoid overbought/oversold traps
✅ Dynamic Stop Loss and Take Profit based on ATR (1:1 risk/reward by default)
✅ Auto alerts in JSON format for integration with bots (like Hombrok)
✅ On-screen dashboard showing Win Rate, number of trades, and cumulative profit
⚙️ Customizable parameters:
ATR period and multiplier
RSI period
Candle body strength filter
Risk/Reward ratio (default 1:1)
Capital per trade (in USDT)
🚀 Best for:
Traders looking to automate trades via TradingView-bot integrations
Both Long and Short trades with clearly defined entry rules
Momentum-based strategies confirmed by volume and price action
📦 How to use:
Add it to your 1H BTC/USDT chart
Set up alerts to trigger your bot
Run backtests and optimize parameters based on your risk profile
Let the bot work while you enjoy the gains! 💰
🔔 Pro tip: Combine this setup with order flow or support/resistance zones for even more accurate entries.
Range Breakout Strategy (AM/PM)Range breakout for back-testing on any asset pair for a give time range, for mechanical trading.
订单流轨迹自动交易脚本《订单流交易》一书系统性地介绍了订单流（Order Flow）这一市场分析方法，强调通过分析市场中每一价位的主动买卖单量，捕捉供需力量的变化，从而预判价格趋势的延续或反转。以下是核心内容提炼：
1. 订单流的核心概念
订单流定义：通过实时追踪每个价位的主动买单和卖单成交量，揭示市场供需力量的动态平衡。与传统K线图（仅显示开盘、收盘、最高、最低价）不同，订单流深入价格内部，展示买卖双方的博弈细节。
关键指标：
Delta：单根K线内主动买单总量减去主动卖单总量的差值，反映多空力量强弱。
POC（成交量最大价位）：K线内部成交量最大的价格点，揭示多空争夺的核心区域。
失衡现象：当某一价位的主动买单量显著高于卖单（需求失衡）或反之（供应失衡），阈值通常设为3:1。
堆积失衡：连续多个价位出现供需失衡，形成支撑/阻力带。
2. 订单流的优势
实时性：直接反映市场当下行为，而非滞后指标（如MACD、RSI）。
识别主力动向：通过大单、微单、被套交易者等信号，捕捉机构或主力资金的痕迹。
大单：顶部/底部成交量显著高于相邻价位，表明主力介入。
微单：顶部/底部成交量骤减，显示趋势末端力量衰竭。
被套交易者：趋势末端大量反向成交，导致价格反转（如顶部放量却无法突破）。
3. 订单流的分析方法
价格与成交量结合：
健康上涨：伴随主动买单递增，Delta为正且放大。
弱势反转：顶部出现需求失衡但价格回落，或底部供应失衡却未创新低。
关键价位的应用：
支撑/阻力：通过前日高低点、VWAP（成交量加权平均价）、月线级别高低点等判断。
突破与回踩：价格突破关键位后回踩确认，结合订单流验证有效性。
4. 交易策略与实战应用
高胜率信号：
顶部/底部微单：趋势末端成交量萎缩，预示反转（如顶部主动买单骤减）。
失衡堆积：连续失衡形成支撑/阻力带，回踩时入场。
吸收与主动出击：价格在区间内震荡（吸收）后突破，伴随成交量放大。
资金管理与心理：
止损设置：基于失衡区间或关键价位，控制单笔亏损（如1-2跳）。
空仓纪律：无明确信号时保持观望，避免过度交易。
5. 与传统技术的对比
K线图的局限：无法展示价格内部成交细节，仅依赖形态（如十字星、吞噬）易被误导。
订单流的独特性：通过微观成交数据，识别市场情绪（如恐慌性抛售或贪婪追涨），避免“看图说话”的滞后性。
6. 适用场景与市场
高流动性市场：期货、股指、外汇等，需足够成交量支撑订单流分析。
日内与波段交易：短周期（如15分钟）捕捉供需突变，长周期验证趋势强度。
总结
订单流的核心在于通过实时成交数据，理解市场参与者的行为逻辑，从而在趋势启动初期或反转前捕捉机会。它并非预测工具，而是通过供需力量对比，为交易决策提供客观依据。结合严格的资金管理和心理纪律，订单流能显著提升交易的胜率与盈亏比。
**"Order Flow Trading"** systematically introduces the market analysis method of **Order Flow**, emphasizing the capture of supply-demand dynamics by analyzing active buy/sell volumes at each price level to anticipate trend continuations or reversals. Below is a distilled summary of the core concepts:
---
### **1. Core Concepts of Order Flow**
- **Definition**:
Order Flow tracks real-time active buy/sell volumes at each price level, revealing the dynamic balance between supply and demand. Unlike traditional candlestick charts (which only show open, close, high, and low prices), Order Flow dives into price internals, exposing the battle between buyers and sellers.
- **Key Metrics**:
- **Delta**: The difference between total active buy volume and sell volume within a single candlestick, reflecting bullish/bearish strength.
- **POC (Point of Control)**: The price level with the highest volume in a candlestick, indicating the focal point of buyer-seller conflict.
- **Imbalance**: Occurs when buy volume significantly exceeds sell volume (**demand imbalance**, threshold ~3:1) or vice versa (**supply imbalance**).
- **Cumulative Imbalance**: Consecutive price levels with imbalances, forming support/resistance zones.
---
### **2. Advantages of Order Flow**
- **Real-Time Insight**: Reflects immediate market behavior, unlike lagging indicators (e.g., MACD, RSI).
- **Identifying Institutional Activity**: Detects "smart money" footprints through signals like:
- **Large Orders**: Unusually high volume at tops/bottoms, signaling institutional participation.
- **Micro Orders**: Abrupt volume drops at trend extremes, indicating exhaustion.
- **Trapped Traders**: Reversals triggered by countertrend volume surges (e.g., failed breakouts with high sell volume).
---
### **3. Analytical Techniques**
- **Price-Volume Integration**:
- **Healthy Rally**: Rising buy volume with expanding positive Delta.
- **Weak Reversal**: Demand imbalance at tops with price rejection, or supply imbalance at bottoms without new lows.
- **Key Price Levels**:
- **Support/Resistance**: Identified via prior highs/lows, VWAP (Volume-Weighted Average Price), or monthly levels.
- **Breakout & Retest**: Validate breakouts using Order Flow after price retests key levels.
---
### **4. Trading Strategies & Execution**
- **High-Probability Signals**:
- **Micro Orders at Extremes**: Volume drying up at tops/bottoms signals reversals.
- **Cumulative Imbalance Zones**: Enter on pullbacks to stacked imbalance areas.
- **Absorption & Breakouts**: Post-consolidation breakouts with volume expansion.
- **Risk & Psychology**:
- **Stop Loss**: Set 1-2 ticks outside imbalance zones or key levels.
- **Flat Discipline**: Avoid overtrading; act only on clear signals.
---
### **5. Order Flow vs. Traditional Analysis**
- **Candlestick Limitations**: Reliance on patterns (e.g., doji, engulfing) often leads to false signals due to missing internal price data.
- **Order Flow Edge**: Leverages granular trade data to detect market sentiment (e.g., panic selling or FOMO buying), avoiding lagging "chart storytelling."
---
### **6. Applicable Markets & Timeframes**
- **High-Liquidity Markets**: Futures, indices, forex (sufficient volume for Order Flow analysis).
- **Timeframes**:
- **Intraday/Swing**: Short-term (e.g., 15min) for sudden supply-demand shifts.
- **Long-Term**: Validate trend strength on higher timeframes (e.g., daily).
---
### **Summary**
Order Flow focuses on interpreting real-time transactional data to decode market participant behavior, capturing opportunities at trend inception or reversal points. It is not a predictive tool but a framework for objective decision-making through supply-demand analysis. Combined with strict risk management and psychological discipline, Order Flow enhances trade accuracy and profit potential.
