Coppock Curve
The Coppock Curve is a long-term momentum indicator, also known as the "Coppock Guide," used to identify potential long-term market turning points, particularly major downturns and upturns, by smoothing the sum of 14-month and 11-month rates of change with a 10-month weighted moving average.
Here's a more detailed breakdown:
What it is:
The Coppock Curve is a technical indicator designed to identify long-term buy and sell signals in major stock market indices and related ETFs.
How it's calculated:
Rate of Change (ROC): The indicator starts by calculating the rate of change (ROC) for 14 and 11 periods (usually months).
Sum of ROCs: The ROC for the 14-period and 11-period are summed.
Weighted Moving Average (WMA): A 10-period weighted moving average (WMA) is then applied to the sum of the ROCs.
Interpreting the Curve:
Buy Signals: A buy signal is often generated when the Coppock Curve crosses above the zero line, suggesting a potential transition from a bearish to a bullish phase.
Sell Signals: While primarily designed to identify market bottoms, some traders may interpret a cross below the zero line as a sell signal or a bearish warning.
Origin and Purpose:
The Coppock Curve was introduced by economist Edwin Coppock in 1962.
It was originally designed to help investors identify opportune moments to enter the market.
Coppock's inspiration came from the Episcopal Church's concept of the average mourning period, which he believed mirrored the stock market's recovery period.
Limitations:
The Coppock Curve is primarily used for long-term analysis and may not be as effective for short-term or intraday trading.
It may lag in rapidly changing markets, and its signals may not always be reliable.
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RSI Pro+ (Bear market, financial crisis and so on EditionIn markets defined by volatility, fear, and uncertainty – the battlegrounds of bear markets and financial crises – you need tools forged in resilience. Introducing RSI Pro+, a strategy built upon a legendary indicator born in 1978, yet engineered with modern visual clarity to remain devastatingly effective even in the chaotic financial landscapes of 3078.
This isn't about complex algorithms predicting the unpredictable. It's about harnessing the raw, time-tested power of the Relative Strength Index (RSI) to identify potential exhaustion points and capitalize on oversold conditions. RSI Pro+ cuts through the noise, providing clear, actionable signals when markets might be poised for a relief bounce or reversal.
Core Technology (The 1978 Engine):
RSI Crossover Entry: The strategy initiates a LONG position when the RSI (default period 11) crosses above a user-defined low threshold (default 30). This classic technique aims to enter when selling pressure may be waning, offering potential entry points during sharp downturns or periods of consolidation after a fall.
Modern Enhancements (The 3078 Cockpit):
RSI Pro+ isn't just about the signal; it's about providing a professional-grade visual experience directly on your chart:
Entry Bar Highlight: A subtle background flash on the chart signals the exact bar where the RSI crossover condition is met, alerting you to potential entry opportunities.
Trade Bar Coloring: Once a trade is active, the price bars are subtly colored, giving you immediate visual confirmation that the strategy is live in the market.
Entry Price Line: A clear, persistent line marks your exact average entry price for the duration of the trade, serving as a crucial visual anchor.
Take Profit Line: Your calculated Take Profit target is plotted as a distinct line, keeping your objective clearly in sight.
Custom Entry Marker: A precise shape (▲) appears below the bar where the trade entry was actually executed, pinpointing the start of the position.
On-Chart Info Table (HUD): A clean, customizable Heads-Up Display appears when a trade is active, showing vital information at a glance:
Entry Price: Your position's average cost basis.
TP Target: The calculated price level for your Take Profit exit.
Current PnL%: Real-time Profit/Loss percentage for the open trade.
Full Customization: Nearly every aspect is configurable via the settings menu:
RSI Period & Crossover Level
Take Profit Percentage
Toggle ALL visual enhancements on/off individually
Position the Info Table wherever you prefer on the chart.
How to Use RSI Pro+:
Add to Chart: Apply the "RSI Pro+ (Bear market...)" strategy to your TradingView chart. Ensure any previous versions are removed.
Access Settings: Click the cogwheel icon (⚙️) next to the strategy name on your chart.
Configure Inputs (Crucial Step):
RSI Crossover Level: This is key. The default (30) targets standard oversold conditions. In severe downturns, you might experiment with lower levels (e.g., 25, 20) or higher ones (e.g., 40) depending on the asset and timeframe. Observe where RSI(11) typically bottoms out on your chart.
Take Profit Percentage (%): Define your desired profit target per trade (e.g., enter 0.5 for 0.5%, 1.0 for 1%). The default is a very small 0.11%.
RSI Period: While default is 11, you can adjust this (e.g., the standard 14).
Visual Enhancements: Enable or disable the visual features (background highlights, bar coloring, lines, markers, table) according to your preference using the checkboxes. Adjust table position.
Observe & Backtest: Watch how the strategy behaves on your chosen asset and timeframe. Use TradingView's Strategy Tester to analyze historical performance based on your settings. No strategy works perfectly everywhere; testing is essential.
Important Considerations:
Risk Management: This specific script version focuses on a Take Profit exit. It does not include an explicit Stop Loss. You MUST manage risk through appropriate position sizing, potentially adding a Stop Loss manually, or by modifying the script.
Oversold ≠ Reversal: An RSI crossover is an indicator of potential exhaustion, not a guarantee of a price reversal.
Fixed TP: A fixed percentage TP ensures small wins but may exit before larger potential moves.
Backtesting Limitations: Past performance does not guarantee future results.
RSI Pro+ strips away complexity to focus on a robust, time-honored principle, enhanced with modern visuals for the discerning trader navigating today's (and tomorrow's) challenging markets
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Pink's Daily SMA Script🚗 This script provides a customizable overlay of seven simple moving averages (SMAs) on the chart. Users can control the display of each SMA by toggling them on or off. The lengths of these SMAs are adjustable, allowing for tailored analysis based on individual preferences.
📊 The script calculates daily SMA values using the request.security() function and plots them as horizontal lines on the chart. These SMAs are updated once per day, typically at the start of the pre-market session (9:00 AM in the "America/New_York" timezone). The script resets the SMA values at the start of each new day, ensuring fresh data for daily analysis.
🕒 In addition to the SMAs, the script includes an optional feature that highlights specific time ranges on the chart: from 11:00 AM to 11:05 AM and from 1:00 PM to 1:30 PM (based on the "America/New_York" timezone). Users can toggle these background highlights on or off, providing visual cues for key times during the trading day. The 11:00 AM window is highlighted in gray, while the 1:00 PM window is highlighted in blue.
🔖 The SMAs are labeled on the right side of the chart, with only one label visible at a time for each SMA. These labels display the length of the respective SMA, and their colors match the lines drawn on the chart, helping to distinguish between the different SMAs.
Special thanks to Pinks333 (www.tradingview.com)
Who provided the logic for the script and was willing to share her logic and open source the script.
ToClrToStrContains functions for conversion of color to string and vice versa:
method toClr( string this ) - converts string reperesentation of color (in hex format) to color
method toHex( color this ) - converts color to string (hex form)
method toRgb( color this ) - converts color to string ("rgb(11,11,11,0)" form)
Thanks to @ImmortalFreedom for his `color_tostring()` function from "RGB Color Fiddler"
itradesize /\ Silver Bullet x Macro x KillzoneThis indicator shows the best way to annotate ICT Killzones, Silver Bullet and Macro times on the chart. With the help of a new pane, it will not distract your chart and will not cause any distractions to your eye, or brain but you can see when will they happen.
The indicator also draws everything beforehand when a proper new day starts.
You can customize them how you want to show up.
Collapsed or full view?
You can hide any of them and keep only the ones you would like to.
All the colors can be customized, texts & sizes or just use shortened texts and you are also able to hide those drawings which are older than the actual day.
You should minimize the pane where the script has been automatically drawn to therefore you will have the best experience and not show any distractions.
The script automatically shows the time-based boxes, based on the New York timezone.
Killzone Time windows ( for indices ):
London KZ 02:00 - 05:00
New York AM KZ 07:00 - 10:00
New York PM KZ 13:30 - 16:00
Silver Bullet times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro times:
02:33 - 03:00
04:03 - 04:30
08:50 - 0910
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Moving Average TransformThe MAT is essentially a different kind of smoothed moving average. It is made to filter out data sets that deviate from the specified absolute threshold and the result becomes a smoothing function. The goal here, inspired by time series analysis within mathematical study, is to eliminate data anomalies and generate a more accurate trendline.
Functionality:
This script calculates a filtered average by:
Determining the mean of the entire data series.
Initializing sum and count variables.
Iterating through the data to filter values that deviate from the mean beyond the threshold.
Calculating a filtered mean based on the filtered data.
The filtered mean is then passed through a moving average function, where various types of moving averages like SMA, EMA, DEMA, TEMA, and ALMA can be applied. Some popular averages such as the HMA were omitted due to their heavy dependency on weighing specific data points.
Some information from "Time Series Analysis" regarding deviations
Definition of Anomaly: An anomaly or outlier is a data point that differs significantly from other observations in the dataset. It can be caused by various reasons such as measurement errors, data entry errors, or genuine extreme observations.
Impact on Mean: The mean (or average) of a dataset is calculated by summing all the values and dividing by the number of values. Since the mean is sensitive to extreme values, even a single outlier can significantly skew the mean.
Example: Consider a simple time series dataset: . The value "150" is an anomaly in this context. If we calculate the mean with this outlier, it is (10 + 12 + 11 + 9 + 150) / 5 = 38.4. However, if we exclude the outlier, the mean becomes (10 + 12 + 11 + 9) / 4 = 10.5. The presence of the outlier has substantially increased the mean.
Accuracy and Representativeness: While the mean calculated without outliers might be more "accurate" in the sense of being more representative of the central tendency of the bulk of the data, it's essential to note that anomalies might convey important information about the system being studied. Blindly removing or ignoring them might lead to overlooking significant events or phenomena.
Approaches to Handle Anomalies?
Detection and Removal
Robust Statistics
Transformation
ICT Silver Bullet [LuxAlgo]The ICT Silver Bullet indicator is inspired from the lectures of "The Inner Circle Trader" (ICT) and highlights the Silver Bullet (SB) window which is a specific 1-hour interval where a Fair Value Gap (FVG) pattern can be formed.
When a FVG is formed during the Silver Bullet window, Support & Resistance lines will be drawn at the end of the SB session.
There are 3 different Silver Bullet windows (New York local time):
The London Open Silver Bullet (3 AM — 4 AM ~ 03:00 — 04:00)
The AM Session Silver Bullet (10 AM — 11 AM ~ 10:00 — 11:00)
The PM Session Silver Bullet (2 PM — 3 PM ~ 14:00 — 15:00)
🔶 USAGE
The ICT Silver Bullet indicator aims to provide users a comprehensive display as similar as possible to how anyone would manually draw the concept on their charts.
It's important to use anything below the 15-minute timeframe to ensure proper setups can display. In this section, we are purely using the 3-minute timeframe.
In the image below, we can see a bullish setup whereas a FVG was successfully retested during the Silver Bullet session. This was then followed by a move upwards to liquidity as our target.
Alternatively, you can also see below a bearish setup utilizing the ICT Silver Bullet indicator outlined.
At this moment, the indicator has removed all other FVGs within the Silver Bullet session & has confirmed this FVG as the retested one.
There is also a support level marked below to be used as a liquidity target as per the ICT Silver Bullet concept suggests.
In the below chart we can see 4 separate consecutive examples of bullish & bearish setups on the 3-minute chart.
🔶 CONCEPTS
This technique can visualize potential support/resistance lines, which can be used as targets.
The script contains 2 main components:
• forming of a Fair Value Gap (FVG)
• drawing support/resistance (S/R) lines
🔹 Forming of FVG
1 basic principle: when a FVG at the end of the SB session is not retraced, it will be made invisible.
Dependable on the settings, different FVG's will be shown.
• 'All FVG': all FVG's are shown, regardless the trend
• 'Only FVG's in the same direction of trend': Only FVG's are shown that are similar to the trend at that moment (trend can be visualized by enabling ' Show ' -> ' Trend ')
-> only bearish FVG when the trend is bearish vs. bullish FVG when trend is bullish
• 'strict': Besides being similar to the trend, only FVG's are shown when the closing price at the end of the SB session is:
– below the top of the FVG box (bearish FVG)
– above bottom of the FVG box (bullish FVG)
• 'super-strict': Besides being similar to the trend, only FVG's are shown when the FVG box is NOT broken
in the opposite direction AND the closing price at the end of the SB session is:
– below bottom of the FVG box (bearish FVG)
– above the top of the FVG box (bullish FVG)
' Super-Strict ' mode resembles ICT lectures the most.
🔹 Drawing support/resistance lines
When the SB session has ended, the script draws potential support/resistance lines, again, dependable on the settings.
• Previous session (any): S/R lines are fetched between current and previous session.
For example, when current session is ' AM SB Session (10 AM — 11 AM) ', then previous session is
' London Open SB (3 AM — 4 AM) ', S/R lines between these 2 sessions alone will be included.
• Previous session (similar): S/R lines are fetched between current and previous - similar - session.
For example, when current session is ' London Open SB (3 AM — 4 AM)' , only S/R lines between
current session and previous ' London Open SB (3 AM — 4 AM) ' session are included.
When a new session starts, S/R lines will be removed, except when enabling ' Keep lines (only in strict mode) '
This is not possible in ' All FVG ' or ' Only FVG's in the same direction of trend ' mode, since the chart would be cluttered.
Note that in ' All FVG ' or ' Only FVG's in the same direction of trend ' mode, both, Support/Resistance lines will be shown,
while in Strict/Super-Strict mode:
• only Support lines will be shown if a bearish FVG appears
• only Resistance lines if a bullish FVG is shown
The lines will still be drawn the the end of the SB session, when a valid FVG appears,
but the S/R lines will remain visible and keep being updated until price reaches that line.
This publication contains a "Minimum Trade Framework (mTFW)", which represents the best-case expected price delivery, this is not your actual trade entry - exit range.
• 40 ticks for index futures or indices
• 15 pips for Forex pairs.
When on ' Strict/Super-Strict ' mode, only S/R lines will be shown which are:
• higher than the lowest FVG bottom + mTFW, in a bullish scenario
• lower than the highest FVG bottom - mTFW, in a bearish scenario
When on ' All FVG/Only FVG's in the same direction of trend ' mode, or on non-Forex/Futures/Indices symbols, S/R needs to be higher/lower than SB session high/low.
🔶 SETTINGS
(Check CONCEPTS for deeper insights and explanation)
🔹 Swing settings (left): Sets the length, which will set the lookback period/sensitivity of the Zigzag patterns (which directs the trend)
🔹 Silver Bullet Session; Show SB session: show lines and labels of SB session
Labels can be disabled separately in the ' Style ' section, color is set at the ' Inputs ' section.
🔹 FVG
– Mode
• All FVG
• Only FVG's in the same direction of trend
• Strict
• Super-Strict
– Colors
– Extend: extend till last bar of SB session
🔹 Targets – support/resistance lines
– Previous session (any): S/R lines fetched between current and previous SB session
– Previous session (similar): S/R lines fetched between current and previous similar SB session
– Colors
– Keep lines (only in strict mode)
🔹 Show
– MSS ~ Session: Show Market Structure Shift , only when this happens during a SB session
– Trend: Show trend (Zigzag, colored ~ trend)
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated
Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA.
This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com
Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types.
STD-Stepped, Variety N-Tuple Moving Averages
STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the calculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code:
temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1))
array.set(workarr, k + 1, temp)
After we pack the array, we apply the coefficients to derive the NTMA:
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Bands/Channels
See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier
Signals
Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short.
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
6 bands/channels types
6 stepping types
Related indicators
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping
STD-Stepped Fast Cosine Transform Moving Average
ATR-Stepped PDF MA
STD-Stepped, Variety N-Tuple Moving Averages [Loxx]STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
STD-Stepped, You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA ; EMA with a depth of 3 is the classic TEMA , and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the caculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
Standard deviation stepping
Variety N-Tuple Moving Averages [Loxx]Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA; EMA with a depth of 3 is the classic TEMA, and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA, the caculation is as follows
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
ema4 = ta.ema(ema3, length)
ema5 = ta.ema(ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Weighted percentile nearest rankYo, posting it for the whole internet, took the whole day to find / to design the actual working solution for weighted percentile 'nearest rank' algorithm, almost no reliable info online and a lot of library-style/textbook-style solutions that don't provide on real world production level.
The principle:
0) initial data
data = 22, 33, 11, 44, 55
weights = 5 , 3 , 2 , 1 , 4
array(s) size = 5
1) sort data array, apply the sorting pattern to the weights array, resulting:
data = 11, 22, 33, 44, 55
weights = 2 , 5 , 3 , 1 , 4
2) get weights cumsum and sum:
weights = 2, 5, 3 , 1 , 4
weights_cum = 2, 7, 10, 11, 15
weights_sum = 15
3) say we wanna find 50th percentile, get a threshold value:
n = 50
thres = weights_sum / 100 * n
7.5 = 15 / 100 * 50
4) iterate through weights_cum until you find a value that >= the threshold:
for i = 0 to size - 1
2 >= 7.5 ? nah
7 >= 7.5 ? nah
10 >= 7.5 ? aye
5) take the iteration index that resulted "aye", and find the data value with the same index, that's gonna be the resulting percentile.
i = 2
data = 33
This one is not an approximation, not an estimator, it's the actual weighted percentile nearest rank as it is.
I tested the thing extensively and it works perfectly.
For the skeptics, check lines 40, 41, 69 in the code, you can comment/uncomment dem to switch for unit (1) weights, resulting in the usual non-weighted percentile nearest rank that ideally matches the TV's built-in function.
Shoutout for @wallneradam for the sorting function mane
...
Live Long and Prosper
z_score_bgd
Z-score indicator for volatile currency pairs, showing STRONG BUY, BUY, SELL, STRONG SELL zones by shading the chart background.
---------------------------------
Background
---------------------------------
Based on mean reversion, a theory that after a swing in price the price will tend back to the mean. This offers some ability to predict future trends.
The formula for calculating a z-score is is z = (x-μ)/σ, where x is the pair price, μ is the mean for a population, and σ is the population standard deviation.
---------------------------------
Set up
---------------------------------
The user can define their own value for the "window" or population, which is the number of preceding days to evaluate. This value will affect the frequency and magnitude of trades, with higher "window" values reducing the frequency of reversions but increasing their magnitude.
Where the value for "window" is left at 99, the default values below will be applied in the background. Otherwise the user's selection will be in effect.
atombtc 18
avaxbtc 21
ethbtc 18
ftmbtc 11
maticbtc 11
solbtc 11
soleth 16
The default values above are intended for the daily time-frame.
---------------------------------
Interpreting the indicator
---------------------------------
Dark green -> large deviation below mean price (strong buy)
Green -> moderate deviation below mean price (buy)
Red -> moderate deviation below mean price (sell)
Dark red -> large deviation below mean price (strong sell)
Z-score is an imperfect indicator, as with all indiciators and trading decisions must be confirmed by multiple indicators and consider other factors.
SuperTrend OptimizerHello!
This indicator attempts to optimize Supertrend parameters. To achieve this, 102 parameter combinations are tested concurrently - the top three performers are listed in descending order.
Parameters,
Factor: Changes to this parameter shifts the tested factor range. For instance, increasing the factor measure from 3.00 to 3.01 (+0.01) will remove 3.00 from the tested range - this setting controls the lower threshold of the range. The upper threshold, in all instances, is the lower Factor threshold + 3.3 (i.e. 3.0(lower) - 6.3(upper), 4.0(lower) - 7.3(upper), 2.5(lower) - 5.8(upper))
ATR period: Changes to this parameter shifts the tested ATR period range. For instance, increasing the ATR measure from 10 to 11 (+1) will remove 10 from the tested range - this setting controls the lower threshold of the range. The upper threshold, in all instances, is the lower threshold + 2 (i.e. 10(lower) - 12(upper), 11(lower) - 13(upper), 9(lower), - 11(upper))
The Factor parameter is modifiable to any positive decimal number; the ATR parameter is modifiable to any positive integer. Changing either parameter shifts the tested parameter combination range. Both parameters can be changed in the settings, to which you control the lower threshold of the range. If, for instance, you were to change the Factor measurement from 3.0 to 4.1 (+1.1) the 4.0 Factor measurement, and all Factor measures less than 4.0, will be excluded from the performance test.
Consequently, a Supertrend test will be performed with a Factor of 4.1 and an ATR period of 10 (default). This test repeats at 0.1 Factor intervals and 1.0 ATR intervals.
Therefore, assume you modify the Factor lower threshold to 3.1 and the ATR lower threshold to 10. The indicator will test three Supertrend systems with a Factor of 3.1 and an ATR period of 10.. then 11.. 12, then three systems with a Factor of 3.2 and an ATR period of 10.. then 11.. 12... until (lower Factor threshold + 3.3) and (lower ATR threshold + 2) are tested... which in this example is... a Factor of 6.4 and an ATR period of 12.
The tested Factor range and ATR range are displayed in a bottom right table alongside the top performing parameter combinations.
Of course, you can change the the lower thresholds, which means you can test numerous Supertrend parameter combinations! However, no greater than 102 parameter combinations will be tested simultaneously; the best performing Supertrend parameters are plotted on the chart automatically.
I will be working on this indicator more tomorrow! Let me know if you have questions or anything you would like included!
(I of course added something fun in the script. Be sure to try it with bar replay!)
Bear & Bull Zone Signal StrategySince I love to mix and match, here is something fresh and that actually works on the breakout of Ethereum without losing your ass on lagging indicators.
It blends some of the nice parts of my previous scripts while moving to big boy pants with a twist on the Fibonacci retracement using SMA and EMA at multiple levels to do a sanity check.
Is it too good to be true? Nope, just what happens when a Solution Architect starts messing around with crypto and applies engineering and mathematics to the mix. You get a strategy that really doesn't have high profit losses when you tweak it just the right way.
What's the right tweak you ask?
1. Start with a 30 minute timeframe and set your window start date to the date the market began the bear or bull run
2. Make sure you can see your strategy performance window (not the graph one)
3. Set Stop Loss and Target Profit to 50%
4. Use your mouse wheel or up and down arrows and mess around with the RSI, go down one at a time but no lower than 7. Whichever value displayed the highest long or short gain is the one to pick.
5. Now select long or short only based on whichever one shows the highest gain.
6. Now go to K and D, leave K as 3 and check what happens when D is 4 or 5. Leave D at the value that gives you the highest gain.
7. Now go to EMA Fast and Slow Lengths. Leave Fast at 5 and check what happens when the Slow is moved up to 11 or 12, do the gains go up. If not, check what happens when Slow is moved down to 9, 8, or 7. Whichever gives you the highest gain, leave it there. Now go mess with the fast length, keep in mind that fast must always be less than slow. So check values down to 3 and up to 6. Same concept, mo money...leave it be.
8. Now go mess with the Target Profit, I start at 5, hit enter, then go to 7, hit enter, then 9...up by 2 until I get to 21 to make sure I don't hastily pick a low one and always keep in mind between which values the gain switched from high to low. For example, in this example I published at 11 it was $5k and at 13 it was $3700 for the gains. So after I got up to 21 I went back to 11 and started going up by 0.01 steps until the value dropped, which was at 11.19 so I set it at 11.18.
9. Now stop loss is trickier, you've maximized the gains, which means if you set the stop loss at a low value you will sacrifice gains. Typically by this point your loss is less than 10% with this script. So, my approach is to find the value where the stop loss doesn't change what I've tweaked already. In this example, I did the same start at 5 and go up by 2 and saw that when I went to 17 it stopped changing. So I started going back down by 0.5 and saw at 15.5 the gains went lower again. Now I started going back up in steps of 0.01 and at 15.98 it went back to the high gain I already tweaked for. I kept stop loss there and unleashed the strategy on ETH.
So far so good, no bad trades and it's been behaving pretty well.
Universal Global SessionUniversal Global Session
This Script combines the world sessions of: Stocks, Forex, Bitcoin Kill Zones, strategic points, all configurable, in a single Script, to capitalize the opening and closing times of global exchanges as investment assets, becoming an Universal Global Session .
It is based on the great work of @oscarvs ( BITCOIN KILL ZONES v2 ) and the scripts of @ChrisMoody. Thank you Oscar and Chris for your excellent judgment and great work.
At the end of this writing you can find all the internet references of the extensive documentation that I present here. To maximize your benefits in the use of this Script, I recommend that you read the entire document to create an objective and practical criterion.
All the hours of the different exchanges are presented at GMT -6. In Market24hClock you can adjust it to your preferences.
After a deep investigation I have been able to show that the different world sessions reveal underlying investment cycles, where it is possible to find sustained changes in the nominal behavior of the trend before the passage from one session to another and in the natural overlaps between the sessions. These underlying movements generally occur 15 minutes before the start, close or overlap of the session, when the session properly starts and also 15 minutes after respectively. Therefore, this script is designed to highlight these particular trending behaviors. Try it, discover your own conclusions and let me know in the notes, thank you.
Foreign Exchange Market Hours
It is the schedule by which currency market participants can buy, sell, trade and speculate on currencies all over the world. It is open 24 hours a day during working days and closes on weekends, thanks to the fact that operations are carried out through a network of information systems, instead of physical exchanges that close at a certain time. It opens Monday morning at 8 am local time in Sydney —Australia— (which is equivalent to Sunday night at 7 pm, in New York City —United States—, according to Eastern Standard Time), and It closes at 5pm local time in New York City (which is equivalent to 6am Saturday morning in Sydney).
The Forex market is decentralized and driven by local sessions, where the hours of Forex trading are based on the opening range of each active country, becoming an efficient transfer mechanism for all participants. Four territories in particular stand out: Sydney, Tokyo, London and New York, where the highest volume of operations occurs when the sessions in London and New York overlap. Furthermore, Europe is complemented by major financial centers such as Paris, Frankfurt and Zurich. Each day of forex trading begins with the opening of Australia, then Asia, followed by Europe, and finally North America. As markets in one region close, another opens - or has already opened - and continues to trade in the currency market. The seven most traded currencies in the world are: the US dollar, the euro, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, and the New Zealand dollar.
Currencies are needed around the world for international trade, this means that operations are not dominated by a single exchange market, but rather involve a global network of brokers from around the world, such as banks, commercial companies, central banks, companies investment management, hedge funds, as well as retail forex brokers and global investors. Because this market operates in multiple time zones, it can be accessed at any time except during the weekend, therefore, there is continuously at least one open market and there are some hours of overlap between the closing of the market of one region and the opening of another. The international scope of currency trading means that there are always traders around the world making and satisfying demands for a particular currency.
The market involves a global network of exchanges and brokers from around the world, although time zones overlap, the generally accepted time zone for each region is as follows:
Sydney 5pm to 2am EST (10pm to 7am UTC)
London 3am to 12 noon EST (8pm to 5pm UTC)
New York 8am to 5pm EST (1pm to 10pm UTC)
Tokyo 7pm to 4am EST (12am to 9am UTC)
Trading Session
A financial asset trading session refers to a period of time that coincides with the daytime trading hours for a given location, it is a business day in the local financial market. This may vary according to the asset class and the country, therefore operators must know the hours of trading sessions for the securities and derivatives in which they are interested in trading. If investors can understand market hours and set proper targets, they will have a much greater chance of making a profit within a workable schedule.
Kill Zones
Kill zones are highly liquid events. Many different market participants often come together and perform around these events. The activity itself can be event-driven (margin calls or option exercise-related activity), portfolio management-driven (asset allocation rebalancing orders and closing buy-in), or institutionally driven (larger players needing liquidity to complete the size) or a combination of any of the three. This intense cross-current of activity at a very specific point in time often occurs near significant technical levels and the established trends emerging from these events often persist until the next Death Zone approaches or enters.
Kill Zones are evolving with time and the course of world history. Since the end of World War II, New York has slowly invaded London's place as the world center for commercial banking. So much so that during the latter part of the 20th century, New York was considered the new center of the financial universe. With the end of the cold war, that leadership appears to have shifted towards Europe and away from the United States. Furthermore, Japan has slowly lost its former dominance in the global economic landscape, while Beijing's has increased dramatically. Only time will tell how these death zones will evolve given the ever-changing political, economic, and socioeconomic influences of each region.
Financial Markets
New York
New York (NYSE Chicago, NASDAQ)
7:30 am - 2:00 pm
It is the second largest currency platform in the world, followed largely by foreign investors as it participates in 90% of all operations, where movements on the New York Stock Exchange (NYSE) can have an immediate effect (powerful) on the dollar, for example, when companies merge and acquisitions are finalized, the dollar can instantly gain or lose value.
A. Complementary Stock Exchanges
Brazil (BOVESPA - Brazilian Stock Exchange)
07:00 am - 02:55 pm
Canada (TSX - Toronto Stock Exchange)
07:30 am - 02:00 pm
New York (NYSE - New York Stock Exchange)
08:30 am - 03:00 pm
B. North American Trading Session
07:00 am - 03:00 pm
(from the beginning of the business day on NYSE and NASDAQ, until the end of the New York session)
New York, Chicago and Toronto (Canada) open the North American session. Characterized by the most aggressive trading within the markets, currency pairs show high volatility. As the US markets open, trading is still active in Europe, however trading volume generally decreases with the end of the European session and the overlap between the US and Europe.
C. Strategic Points
US main session starts in 1 hour
07:30 am
The euro tends to drop before the US session. The NYSE, CHX and TSX (Canada) trading sessions begin 1 hour after this strategic point. The North American session begins trading Forex at 07:00 am.
This constitutes the beginning of the overlap of the United States and the European market that spans from 07:00 am to 10:35 am, often called the best time to trade EUR / USD, it is the period of greatest liquidity for the main European currencies since it is where they have their widest daily ranges.
When New York opens at 07:00 am the most intense trading begins in both the US and European markets. The overlap of European and American trading sessions has 80% of the total average trading range for all currency pairs during US business hours and 70% of the total average trading range for all currency pairs during European business hours. The intersection of the US and European sessions are the most volatile overlapping hours of all.
Influential news and data for the USD are released between 07:30 am and 09:00 am and play the biggest role in the North American Session. These are the strategically most important moments of this activity period: 07:00 am, 08:00 am and 08:30 am.
The main session of operations in the United States and Canada begins
08:30 am
Start of main trading sessions in New York, Chicago and Toronto. The European session still overlaps the North American session and this is the time for large-scale unpredictable trading. The United States leads the market. It is difficult to interpret the news due to speculation. Trends develop very quickly and it is difficult to identify them, however trends (especially for the euro), which have developed during the overlap, often turn the other way when Europe exits the market.
Second hour of the US session and last hour of the European session
09:30 am
End of the European session
10:35 am
The trend of the euro will change rapidly after the end of the European session.
Last hour of the United States session
02:00 pm
Institutional clients and very large funds are very active during the first and last working hours of almost all stock exchanges, knowing this allows to better predict price movements in the opening and closing of large markets. Within the last trading hours of the secondary market session, a pullback can often be seen in the EUR / USD that continues until the opening of the Tokyo session. Generally it happens if there was an upward price movement before 04:00 pm - 05:00 pm.
End of the trade session in the United States
03:00 pm
D. Kill Zones
11:30 am - 1:30 pm
New York Kill Zone. The United States is still the world's largest economy, so by default, the New York opening carries a lot of weight and often comes with a huge injection of liquidity. In fact, most of the world's marketable assets are priced in US dollars, making political and economic activity within this region even more important. Because it is relatively late in the world's trading day, this Death Zone often sees violent price swings within its first hour, leading to the proven adage "never trust the first hour of trading in America. North.
---------------
London
London (LSE - London Stock Exchange)
02:00 am - 10:35 am
Britain dominates the currency markets around the world, and London is its main component. London, a central trading capital of the world, accounts for about 43% of world trade, many Forex trends often originate from London.
A. Complementary Stock Exchange
Dubai (DFM - Dubai Financial Market)
12:00 am - 03:50 am
Moscow (MOEX - Moscow Exchange)
12:30 am - 10:00 am
Germany (FWB - Frankfurt Stock Exchange)
01:00 am - 10:30 am
Afríca (JSE - Johannesburg Stock Exchange)
01:00 am - 09:00 am
Saudi Arabia (TADAWUL - Saudi Stock Exchange)
01:00 am - 06:00 am
Switzerland (SIX - Swiss Stock Exchange)
02:00 am - 10:30 am
B. European Trading Session
02:00 am - 11:00 am
(from the opening of the Frankfurt session to the close of the Order Book on the London Stock Exchange / Euronext)
It is a very liquid trading session, where trends are set that start during the first trading hours in Europe and generally continue until the beginning of the US session.
C. Middle East Trading Session
12:00 am - 06:00 am
(from the opening of the Dubai session to the end of the Riyadh session)
D. Strategic Points
European session begins
02:00 am
London, Frankfurt and Zurich Stock Exchange enter the market, overlap between Europe and Asia begins.
End of the Singapore and Asia sessions
03:00 am
The euro rises almost immediately or an hour after Singapore exits the market.
Middle East Oil Markets Completion Process
05:00 am
Operations are ending in the European-Asian market, at which time Dubai, Qatar and in another hour in Riyadh, which constitute the Middle East oil markets, are closing. Because oil trading is done in US dollars, and the region with the trading day coming to an end no longer needs the dollar, consequently, the euro tends to grow more frequently.
End of the Middle East trading session
06:00 am
E. Kill Zones
5:00 am - 7:00 am
London Kill Zone. Considered the center of the financial universe for more than 500 years, Europe still has a lot of influence in the banking world. Many older players use the European session to establish their positions. As such, the London Open often sees the most significant trend-setting activity on any trading day. In fact, it has been suggested that 80% of all weekly trends are set through the London Kill Zone on Tuesday.
F. Kill Zones (close)
2:00 pm - 4:00 pm
London Kill Zone (close).
---------------
Tokyo
Tokyo (JPX - Tokyo Stock Exchange)
06:00 pm - 12:00 am
It is the first Asian market to open, receiving most of the Asian trade, just ahead of Hong Kong and Singapore.
A. Complementary Stock Exchange
Singapore (SGX - Singapore Exchange)
07:00 pm - 03:00 am
Hong Kong (HKEx - Hong Kong Stock Exchange)
07:30 pm - 02:00 am
Shanghai (SSE - Shanghai Stock Exchange)
07:30 pm - 01:00 am
India (NSE - India National Stock Exchange)
09:45 pm - 04:00 am
B. Asian Trading Session
06:00 pm - 03:00 am
From the opening of the Tokyo session to the end of the Singapore session
The first major Asian market to open is Tokyo which has the largest market share and is the third largest Forex trading center in the world. Singapore opens in an hour, and then the Chinese markets: Shanghai and Hong Kong open 30 minutes later. With them, the trading volume increases and begins a large-scale operation in the Asia-Pacific region, offering more liquidity for the Asian-Pacific currencies and their crosses. When European countries open their doors, more liquidity will be offered to Asian and European crossings.
C. Strategic Points
Second hour of the Tokyo session
07:00 pm
This session also opens the Singapore market. The commercial dynamics grows in anticipation of the opening of the two largest Chinese markets in 30 minutes: Shanghai and Hong Kong, within these 30 minutes or just before the China session begins, the euro usually falls until the same moment of the opening of Shanghai and Hong Kong.
Second hour of the China session
08:30 pm
Hong Kong and Shanghai start trading and the euro usually grows for more than an hour. The EUR / USD pair mixes up as Asian exporters convert part of their earnings into both US dollars and euros.
Last hour of the Tokyo session
11:00 pm
End of the Tokyo session
12:00 am
If the euro has been actively declining up to this time, China will raise the euro after the Tokyo shutdown. Hong Kong, Shanghai and Singapore remain open and take matters into their own hands causing the growth of the euro. Asia is a huge commercial and industrial region with a large number of high-quality economic products and gigantic financial turnover, making the number of transactions on the stock exchanges huge during the Asian session. That is why traders, who entered the trade at the opening of the London session, should pay attention to their terminals when Asia exits the market.
End of the Shanghai session
01:00 am
The trade ends in Shanghai. This is the last trading hour of the Hong Kong session, during which market activity peaks.
D. Kill Zones
10:00 pm - 2:00 am
Asian Kill Zone. Considered the "Institutional" Zone, this zone represents both the launch pad for new trends as well as a recharge area for the post-American session. It is the beginning of a new day (or week) for the world and as such it makes sense that this zone often sets the tone for the remainder of the global business day. It is ideal to pay attention to the opening of Tokyo, Beijing and Sydney.
--------------
Sidney
Sydney (ASX - Australia Stock Exchange)
06:00 pm - 12:00 am
A. Complementary Stock Exchange
New Zealand (NZX - New Zealand Stock Exchange)
04:00 pm - 10:45 pm
It's where the global trading day officially begins. While it is the smallest of the megamarkets, it sees a lot of initial action when markets reopen Sunday afternoon as individual traders and financial institutions are trying to regroup after the long hiatus since Friday afternoon. On weekdays it constitutes the end of the current trading day where the change in the settlement date occurs.
B. Pacific Trading Session
04:00 pm - 12:00 am
(from the opening of the Wellington session to the end of the Sydney session)
Forex begins its business hours when Wellington (New Zealand Exchange) opens local time on Monday. Sydney (Australian Stock Exchange) opens in 2 hours. It is a session with a fairly low volatility, configuring itself as the calmest session of all. Strong movements appear when influential news is published and when the Pacific session overlaps the Asian Session.
C. Strategic Points
End of the Sydney session
12:00 am
---------------
Conclusions
The best time to trade is during overlaps in trading times between open markets. Overlaps equate to higher price ranges, creating greater opportunities.
Regarding press releases (news), it should be noted that these in the currency markets have the power to improve a normally slow trading period. When a major announcement is made regarding economic data, especially when it goes against the predicted forecast, the coin can lose or gain value in a matter of seconds. In general, the more economic growth a country produces, the more positive the economy is for international investors. Investment capital tends to flow to countries that are believed to have good growth prospects and subsequently good investment opportunities, leading to the strengthening of the country's exchange rate. Also, a country that has higher interest rates through its government bonds tends to attract investment capital as foreign investors seek high-yield opportunities. However, stable economic growth and attractive yields or interest rates are inextricably intertwined. It's important to take advantage of market overlaps and keep an eye out for press releases when setting up a trading schedule.
References:
www.investopedia.com
www.investopedia.com
www.investopedia.com
www.investopedia.com
market24hclock.com
market24hclock.com
Back to zero: Understanding seriestype: pine series basic example
time required: 10 minutes
level: medium (need to know the "array" data variable as a generic programming concept, basic Pine syntax)
tl;dr how variables and series work in Pine
Pine is an array/vector language. That's something that twists how it behaves, and how we have to think about it. A lot of misunderstandings come from forgetting this fact. This example tries to clear that concept.
First, you need to know what an array is, and how it works in a programmig language. Also, having javascript under your belt helps too. If you don't, google "javascript array basic tutorial" is your friend :)
So, in pine arrays are called "series". Every variable is an array with values for each candle in the chart. if we do:
myVar = true
this is not a constant. It is a series of values for each candle, { true, true,....., true }
In practice, the result is the same, but we can access each of the values in the series, like myVar{0}, myVar{7}, myVar{anyNumber}....
Again, it is not a constant, since you can access/modify the each value individually
so, lets show it:
plot (myVar, clolor = gray)
this plots an horizontal line of value 1 ( 1 is equal to true ) so it's all good.
On to a more usual series:
tipicalSeries = close > open ? true : false
plot(tipicalSeries, color= blue)
This gives the expected result, a tipical up and down line with values at 1 or 0. Naturally, "tipicalSeries" is an array, the "ups" and "downs" are all stored under the same variable, indexed by the candles.
In Pine, the ZERO position in the array is the last one, which corresponds to the last candle on the right. Say you have a chart with 12 candles. The close would be the closing value of what we intuitively think as first candle, the one on the left. then close ... and so on.... until close , the value of the "last" candle, the one on the right. It actually helps to start thinking of the positions backwards, counting down to zero, rocket launch style :)
And back to our series. The myVar will also be the same size, from myVar to myVar .
When we do some operation with them, something simple like
if ( myVar == tipicalSeries)
what is really happening is that internally, Pine is checking each of the indexes, as in myVar == tipicalSeries , myVar == tipicalSeries .... myVar == tipicalSeries
And we can store that stuff to check it. simply:
result = (myVar == tipicalSeries) ? true : false //yes, this is the same as tipicalSeries, but we're not in a boolean logic tut ;)
plot (result)
The reason we can plot the result is that it is an array, not a single value. The example indicator i provide shows a plot where the values are obtained from different places in the array, this line here:
mySeries3 = mySeries2 and mySeries1
this creates a series that is the result of the PREVIOUS values stored (the zero index is the one most at the right, or the "current" one), which here just causes a shift in the plotted line by one candle.
Go ahead, grab a copy of my code, try to change the indexes and see the results. Understanding this stuff is critical to go deeper into Pine :)
Futubull VWAPTo apply Futubull’s VWAP (Volume Weighted Average Price) indicator to TradingView, the key is to understand Futubull’s VWAP calculation logic and features, then replicate them using TradingView’s Pine Script language. Below are detailed steps and methods, incorporating the provided context and prior discussions, to help you create a custom VWAP indicator in TradingView that mirrors Futubull’s functionality. The script will be tailored for day trading CIEN (Ciena Corporation) on September 4, 2025, during pre-market (Hong Kong time 11:25 PM, equivalent to US Eastern Time 11:25 AM), leveraging its earnings-driven breakout ($115.50, +21.81%).
BRDN SCANNER - KS CONFIRMATIONS - GOLDBRDN SCANNER:
ENGLISH
Goal: ultra-selective, trend-following entries with automatic confirmations (no manual checks). Optimized presets for Gold (XAUUSD) and US30. The POI/POC logic runs internally (no line drawn). A signal prints only when all filters agree.
What it does
Trend: EMA-20 > EMA-50 > EMA-238 (or reverse) + optional HTF trend and slope.
POI control (internal): Rolling/Session VWAP with a neutral band →
BUY only when price is above the band (buyers in control), SELL only when below (sellers in control).
Volume: current volume > factor × SMA(volume). You can source futures volume (e.g., GC1! for Gold, YM1! for US30) while trading a CFD/spot chart.
Volatility filter: ATR% minimum to avoid chop.
Momentum: RSI > 50 or RSI cross 50 (selectable).
Candle match (pick one):
Breakout close (close > prior high / < prior low + body requirement)
Engulfing (classical body engulf + body requirement)
Strong close (closes near range extreme with short tail)
Sessions: optional trading windows for liquid hours.
Alerts: Buy / Sell / Combined + optional rich alert() messages.
Presets
Gold (XAUUSD) (default)
Volume source GC1!, HTF 60m, Neutral band ~ 0.35%.
Momentum: RSI cross 50 · Candle: Breakout close · Close-only ON.
Sessions: 08:00–11:00 & 13:30–17:00 (chart’s exchange timezone).
US30 (Dow)
Volume source YM1!, HTF 60m, Neutral band ~ 0.35%.
Momentum: RSI cross 50 · Candle: Strong close · Close-only ON.
Session: 09:30–16:00 (US cash hours).
Custom
Full manual control over all inputs.
Signal rules (summary)
BUY arrow = TrendBull ∧ (Slope optional) ∧ RSI ok ∧ HighVolume ∧ ATR ok ∧ Above VWAP band ∧ Not neutral ∧ CandleMatch.
SELL arrow = same, but below the band.
Suggested timeframes
5m–15m for intraday (Gold & US30). Keep Close-only = ON for cleaner signals, especially with MTF.
How to set alerts
Add the indicator → Add alert.
Condition: this indicator → choose Buy, Sell, or Combined.
Frequency: Once per bar close (recommended) or Once per bar.
For rich texts, choose Any alert() function call.
Tuning tips
Too many signals → raise VolFactor, NeutralBand, or Min ATR%; pick a stricter Candle mode.
Too few signals → lower one parameter at a time (e.g., VolFactor 1.8 → 1.5).
Prefer futures volume inputs for more reliable volume on CFD/spot charts.
Notes
No indicator guarantees a win rate. Back/forward test per instrument, timeframe, and market regime, and use sound risk management.
NEDERLANDS:
Doel: super-selectieve trend-volgende entries met automatische confirmaties (geen handwerk), geoptimaliseerd voor Gold (XAUUSD) en US30. De “POI/POC” gebeurt intern (geen lijn op de chart), pas een pijl als álle filters groen staan.
Wat doet het?
Trend: EMA-20 > EMA-50 > EMA-238 (of omgekeerd) + optionele HTF-trend en slope.
POI-control (intern): Rolling/Session VWAP met neutral band → alleen BUY als prijs boven band (buyers in control), SELL als onder (sellers in control).
Volume: huidig volume > factor × SMA(volume). Optioneel externe futures-volume (bv. GC1! voor Gold, YM1! voor US30).
Volatiliteit: ATR%-chopfilter (te lage volatiliteit = geen signaal).
Momentum: RSI > 50 of RSI cross 50.
Candle-match (keuze):
Breakout close (close > vorige high / < vorige low + body-eis)
Engulfing (klassiek omarmen + body-eis)
Strong close (sluit in top/bodem van range, korte staart)
Sessies: optionele tijdvensters voor liquid uren.
Alerts: Buy / Sell / Combined en optioneel rijke alert()-berichten.
Presets
Gold (XAUUSD) (default)
Volume bron: GC1! | HTF: 60m | Neutral band ~ 0.35%
Momentum: RSI cross 50 | Candle: Breakout close | Close-only: ON
Sessies: 08:00–11:00 & 13:30–17:00 (exchange-tz van chart)
US30 (Dow)
Volume bron: YM1! | HTF: 60m | Neutral band ~ 0.35%
Momentum: RSI cross 50 | Candle: Strong close | Close-only: ON
Sessie: 09:30–16:00 (US cash hours)
Custom
Handmatige controle over alle inputs.
Signaal-criteria (samengevat)
BUY-pijl = TrendBull ∧ (Slope optioneel) ∧ RSI-oké ∧ HoogVolume ∧ ATR-oké ∧ Boven VWAP-band ∧ Niet neutraal ∧ Candle-match.
SELL-pijl = analoog, maar onder VWAP-band.
Aanbevolen timeframes
5m–15m voor intraday (Gold & US30).
Close-only = ON voor cleanere signalen (zeker met MTF).
Alerts
Voeg indicator toe → klik Add alert.
Condition: indicatornaam → kies Buy, Sell of Combined.
Frequentie: Once per bar close (aanrader) of Once per bar.
Wil je rijke teksten? Kies Any alert() function call.
Tuning tips
Te veel signalen → verhoog VolFactor, NeutralBand of Min ATR%; kies strengere Candle.
Te weinig signalen → verlaag één parameter per keer (bijv. VolFactor 1.8 → 1.5).
Gebruik futures-volume voor betrouwbaardere volume-input op CFD/spot charts.
Belangrijk
Geen enkele indicator garandeert een winrate. Test per instrument/timeframe/marktregime (back/forward test) en combineer met risk-management. Gebruik alerts verantwoord.
DTC Intra+DTC Intra+
Complete Indian Intraday Trading Indicator
What This Indicator Does:
DTC Intra+ is a comprehensive intraday trading indicator designed specifically for Indian markets. It provides real-time session analysis, volume profiling, and technical insights that work consistently across all timeframes.
Key Features:
Multi-Timeframe Data Consistency
Solves the common problem where indicators show different values on different timeframes
Gap percentage, ADR (Average Daily Range), and Burst values remain consistent whether viewing 1-minute or daily charts
Uses proprietary algorithms to fetch daily data and apply it accurately to intraday calculations
Intelligent Session Detection
Automatically detects and displays three Indian market sessions: Morning (9:15-11:00 AM), Mid-Day (11:00-1:30 PM), Afternoon (1:30-3:30 PM)
Creates visual session boxes that extend dynamically and finalize at session boundaries
Each session maintains distinct colors and labels for easy identification
Advanced Volume Analysis
Calculates Relative Volume (RVol) using 21-period moving average for volume strength assessment
Colors candles based on volume strength: high/moderate bullish and bearish volume patterns
Customizable volume thresholds (150% and 200% of average volume) for precise signal generation
Pattern Recognition System
Identifies mini-coil consolidation patterns with customizable styling options
Maintains visual persistence across timeframes and chart updates
Configurable lookback periods and pattern validation algorithms
Technical Analysis Tools
Four customizable Moving Averages (10, 20, 50, 200) with multiple calculation types (SMA, EMA, WMA, HMA)
VWAP integration with theme-adaptive styling
Gap analysis with customizable thresholds (1% and 2%+) and visual alerts
Theme-Adaptive Interface
Automatically detects and adapts to dark or light chart themes
Professional color schemes optimized for both theme types
Customizable transparency and styling for all visual elements
Information Dashboard
Dynamic information table displaying Gap %, RVol, ADR, Burst, Sector, Industry, Theme, and Session data
Real-time sector and industry classification from TradingView data
Burst Ranker with descriptive labels (GREAT, GOOD, LOW) based on historical performance analysis
How It Works:
Session Management:
The indicator uses time-based detection algorithms to identify Indian market sessions, creating visual boxes that extend dynamically and finalize at session boundaries. Each session maintains its own color scheme and label system.
Data Consistency Engine:
Proprietary cross-timeframe calculation system ensures that daily metrics display consistently across all timeframes by fetching daily data and applying it to intraday calculations.
Volume Profiling:
Custom volume analysis algorithms calculate relative volume strength and apply color coding to candles based on volume thresholds and price direction, providing immediate visual feedback on market activity.
Pattern Detection:
Advanced consolidation pattern recognition system that identifies mini-coil formations using customizable parameters and maintains visual persistence across chart updates.
How To Use:
Apply to any Indian stock chart (BSE, NSDL) on intraday timeframes
Session times are pre-configured for Indian markets but can be customized
Moving Averages can be adjusted to your preferred lengths and calculation types
Volume thresholds can be modified based on your trading strategy
Monitor the information panel for real-time market insights
Use session boxes to identify optimal trading windows
Who This Is For:
Indian intraday traders seeking comprehensive market analysis
Traders who need consistent data across multiple timeframes
Volume-based traders requiring real-time volume strength analysis
Technical analysts needing session-based market structure insights
Busy professionals who want market insights without constant chart monitoring
What Makes It Unique:
Unlike basic session indicators, DTC Intra+ provides cross-timeframe data consistency, advanced volume profiling, and comprehensive Indian market analysis in a single, theme-adaptive interface. The proprietary algorithms ensure accurate daily metrics on intraday charts, making it essential for serious Indian market traders who need reliable, consistent data across all timeframes.
The Bottom Line:
This indicator transforms how you view Indian intraday markets by providing consistent, reliable data across all timeframes while offering advanced session analysis, volume profiling, and pattern recognition. It's designed specifically for Indian market characteristics and trading patterns, giving you the tools to trade with confidence and precision.
NY Sessions Boxes (Live Drawing)//@version=5
indicator("NY Sessions Boxes (Live Drawing)", overlay=true)
ny_tz = "America/New_York"
t = time(timeframe.period, ny_tz)
hour_ny = hour(t)
minute_ny = minute(t)
// سشن ۱: 02:00 – 05:00
session1_active = (hour_ny >= 2 and hour_ny < 5)
session1_start = (hour_ny == 2 and minute_ny == 0)
// سشن ۲: 09:30 – 11:00
session2_active = ((hour_ny == 9 and minute_ny >= 30) or (hour_ny > 9 and hour_ny < 11))
session2_start = (hour_ny == 9 and minute_ny == 30)
var box box1 = na
var float hi1 = na
var float lo1 = na
if session1_start
hi1 := high
lo1 := low
box1 := box.new(left = time, right = time, top = high, bottom = low, bgcolor=color.new(color.blue, 85), border_color=color.blue)
if session1_active and not na(box1)
hi1 := math.max(hi1, high)
lo1 := math.min(lo1, low)
box.set_right(box1, time)
box.set_top(box1, hi1)
box.set_bottom(box1, lo1)
if not session1_active and not na(box1)
box1 := na
hi1 := na
lo1 := na
var box box2 = na
var float hi2 = na
var float lo2 = na
if session2_start
hi2 := high
lo2 := low
box2 := box.new(left = time, right = time, top = high, bottom = low, bgcolor=color.new(color.purple, 85), border_color=color.purple)
if session2_active and not na(box2)
hi2 := math.max(hi2, high)
lo2 := math.min(lo2, low)
box.set_right(box2, time)
box.set_top(box2, hi2)
box.set_bottom(box2, lo2)
if not session2_active and not na(box2)
box2 := na
hi2 := na
lo2 := na
EAOBS by MIGVersion 1
1. Strategy Overview Objective: Capitalize on breakout movements in Ethereum (ETH) price after the Asian open pre-market session (7:00 PM–7:59 PM EST) by identifying high and low prices during the session and trading breakouts above the high or below the low.
Timeframe: Any (script is timeframe-agnostic, but align with session timing).
Session: Pre-market session (7:00 PM–7:59 PM EST, adjustable for other time zones, e.g., 12:00 AM–12:59 AM GMT).
Risk-Reward Ratios (R:R): Targets range from 1.2:1 to 5.2:1, with a fixed stop loss.
Instrument: Ethereum (ETH/USD or ETH-based pairs).
2. Market Setup Session Monitoring: Monitor ETH price action during the pre-market session (7:00 PM–7:59 PM EST), which aligns with the Asian market open (e.g., 9:00 AM–9:59 AM JST).
The script tracks the highest high and lowest low during this session.
Breakout Triggers: Buy Signal: Price breaks above the session’s high after the session ends (7:59 PM EST).
Sell Signal: Price breaks below the session’s low after the session ends.
Visualization: The session is highlighted on the chart with a white background.
Horizontal lines are drawn at the session’s high and low, extended for 30 bars, along with take-profit (TP) and stop-loss (SL) levels.
3. Entry Rules Long (Buy) Entry: Enter a long position when the price breaks above the session’s high price after 7:59 PM EST.
Entry price: Just above the session high (e.g., add a small buffer, like 0.1–0.5%, to avoid false breakouts, depending on volatility).
Short (Sell) Entry: Enter a short position when the price breaks below the session’s low price after 7:59 PM EST.
Entry price: Just below the session low (e.g., subtract a small buffer, like 0.1–0.5%).
Confirmation: Use a candlestick close above/below the breakout level to confirm the entry.
Optionally, add volume confirmation or a momentum indicator (e.g., RSI or MACD) to filter out weak breakouts.
Position Size: Calculate position size based on risk tolerance (e.g., 1–2% of account per trade).
Risk is determined by the stop-loss distance (10 points, as defined in the script).
4. Exit Rules Take-Profit Levels (in points, based on script inputs):TP1: 12 points (1.2:1 R:R).
TP2: 22 points (2.2:1 R:R).
TP3: 32 points (3.2:1 R:R).
TP4: 42 points (4.2:1 R:R).
TP5: 52 points (5.2:1 R:R).
Example for Long: If session high is 3000, TP levels are 3012, 3022, 3032, 3042, 3052.
Example for Short: If session low is 2950, TP levels are 2938, 2928, 2918, 2908, 2898.
Strategy: Scale out of the position (e.g., close 20% at TP1, 20% at TP2, etc.) or take full profit at a preferred TP level based on market conditions.
Stop-Loss: Fixed at 10 points from the entry.
Long SL: Session high - 10 points (e.g., entry at 3000, SL at 2990).
Short SL: Session low + 10 points (e.g., entry at 2950, SL at 2960).
Trailing Stop (Optional):After reaching TP2 or TP3, consider trailing the stop to lock in profits (e.g., trail by 10–15 points below the current price).
5. Risk Management per Trade: Limit risk to 1–2% of your trading account per trade.
Calculate position size: Account Size × Risk % ÷ (Stop-Loss Distance × ETH Price per Point).
Example: $10,000 account, 1% risk = $100. If SL = 10 points and 1 point = $1, position size = $100 ÷ 10 = 0.1 ETH.
Daily Risk Limit: Cap daily losses at 3–5% of the account to avoid overtrading.
Maximum Exposure: Avoid taking both long and short positions simultaneously unless using separate accounts or strategies.
Volatility Consideration: Adjust position size during high-volatility periods (e.g., major news events like Ethereum upgrades or macroeconomic announcements).
6. Trade Management Monitoring :Watch for breakouts after 7:59 PM EST.
Monitor price action near TP and SL levels using alerts or manual checks.
Trade Duration: Breakout lines extend for 30 bars (script parameter). Close trades if no TP or SL is hit within this period, or reassess based on market conditions.
Adjustments: If the market shows strong momentum, consider holding beyond TP5 with a trailing stop.
If the breakout fails (e.g., price reverses before TP1), exit early to minimize losses.
7. Additional Considerations Market Conditions: The 7:00 PM–7:59 PM EST session aligns with the Asian market open (e.g., Tokyo Stock Exchange open at 9:00 AM JST), which may introduce higher volatility due to Asian trading activity.
Avoid trading during low-liquidity periods or extreme volatility (e.g., major crypto news).
Check for upcoming events (e.g., Ethereum network upgrades, ETF decisions) that could impact price.
Backtesting: Test the strategy on historical ETH data using the session high/low breakouts for the 7:00 PM–7:59 PM EST window to validate performance.
Adjust TP/SL levels based on backtest results if needed.
Broker and Fees: Use a low-fee crypto exchange (e.g., Binance, Kraken, Coinbase Pro) to maximize R:R.
Account for trading fees and slippage in your position sizing.
Time zone Adjustment: Adjust session time input for your time zone (e.g., "0000-0059" for GMT).
Ensure your trading platform’s clock aligns with the script’s time zone (default: America/New_York).
8. Example Trade Scenario: Session (7:00 PM–7:59 PM EST) records a high of 3050 and a low of 3000.
Long Trade: Entry: Price breaks above 3050 (e.g., enter at 3051).
TP Levels: 3063 (TP1), 3073 (TP2), 3083 (TP3), 3093 (TP4), 3103 (TP5).
SL: 3040 (3050 - 10).
Position Size: For a $10,000 account, 1% risk = $100. SL = 11 points ($11). Size = $100 ÷ 11 = ~0.09 ETH.
Short Trade: Entry: Price breaks below 3000 (e.g., enter at 2999).
TP Levels: 2987 (TP1), 2977 (TP2), 2967 (TP3), 2957 (TP4), 2947 (TP5).
SL: 3010 (3000 + 10).
Position Size: Same as above, ~0.09 ETH.
Execution: Set alerts for breakouts, enter with limit orders, and monitor TPs/SL.
9. Tools and Setup Platform: Use TradingView to implement the Pine Script and visualize breakout levels.
Alerts: Set price alerts for breakouts above the session high or below the session low after 7:59 PM EST.
Set alerts for TP and SL levels.
Chart Settings: Use a 1-minute or 5-minute chart for precise session tracking.
Overlay the script to see high/low lines, TP levels, and SL levels.
Optional Indicators: Add RSI (e.g., avoid overbought/oversold breakouts) or volume to confirm breakouts.
10. Risk Warnings Crypto Volatility: ETH is highly volatile; unexpected news can cause rapid price swings.
False Breakouts: Breakouts may fail, especially in low-volume sessions. Use confirmation signals.
Leverage: Avoid high leverage (e.g., >5x) to prevent liquidation during volatile moves.
Session Accuracy: Ensure correct session timing for your time zone to avoid misaligned entries.
11. Performance Tracking Journaling :Record each trade’s entry, exit, R:R, and outcome.
Note market conditions (e.g., trending, ranging, news-driven).
Review: Weekly: Assess win rate, average R:R, and adherence to the plan.
Monthly: Adjust TP/SL or session timing based on performance.
SCTI V30Description
The SCTI V30 is an advanced multi-functional technical analysis indicator for TradingView that combines multiple analytical approaches into a single comprehensive tool. This indicator provides:
Multiple Moving Average Types (EMA, SMA, PMA with various calculation methods)
Customizable VWAP with standard deviation bands
Sophisticated Divergence Detection across 12 different indicators
Volume Profile Analysis with peak/trough detection
Highly Configurable Display Options
The indicator is designed to help traders identify trends, potential reversals, and key support/resistance levels across different timeframes.
Features
1. Moving Average Systems
EMA Section: 13 configurable EMA periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584)
SMA Section: 13 configurable SMA periods (same as EMA)
PMA Section: 11 customizable moving averages with multiple calculation methods:
ALMA, EMA, RMA, SMA, SWMA, VWAP, VWMA, WMA
Adjustable lengths from 12 to 1056
Customizable colors, widths, and fill options between MAs
2. VWAP Implementation
Multiple anchor periods (Session, Week, Month, Quarter, Year, etc.)
Standard deviation or percentage-based bands
Option to hide on daily/weekly/monthly timeframes
Customizable band multipliers (1.0, 2.0, 3.0)
3. Divergence Detection
Detects regular and hidden divergences across 12 indicators:
MACD, MACD Histogram, RSI, Stochastic, CCI, Momentum
OBV, VW-MACD, Chaikin Money Flow, Money Flow Index
Williams %R, and custom external indicators
Customizable detection parameters:
Pivot point period (1-50)
Source (Close or High/Low)
Divergence type (Regular, Hidden, or Both)
Minimum number of divergences required (1-11)
Maximum pivot points to check (1-20)
Maximum bars to look back (30-200)
4. Volume Profile Analysis
Configurable profile length (10-5000 bars)
Value Area threshold (0-100%)
Profile placement (Left or Right)
Number of rows (30-130)
Profile width adjustment
Volume node detection:
Peaks (with cluster option)
Troughs (with cluster option)
Highest/Lowest volume nodes
Customizable colors for all elements
Input Parameters
The indicator is organized into 7 parameter groups:
Basic Indicator Settings - Toggle visibility of main components
EMA Settings - Configure 13 EMA periods and visibility
SMA Settings - Configure 13 SMA periods and visibility
PMA Settings - Advanced moving average configuration
VWAP Settings - Volume-weighted average price configuration
Divergence Settings - Comprehensive divergence detection options
Volume Profile & Node Detection - Volume analysis configuration
How to Use
Trend Identification: Use the multiple moving averages to identify trend direction and strength. The Fibonacci-based periods (21, 34, 55, 89, 144, etc.) are particularly useful for this.
Support/Resistance: The VWAP and volume profile components help identify key support/resistance levels.
Divergence Trading: Look for divergences between price and the various indicators to spot potential reversal points.
Volume Analysis: The volume profile shows where the most trading activity occurred, highlighting important price levels.
Customization: Adjust the settings to match your trading style and timeframe. The indicator is highly configurable to suit different trading approaches.
Alerts
The indicator includes alert conditions for:
Positive regular divergence detected
Negative regular divergence detected
Positive hidden divergence detected
Negative hidden divergence detected
Any positive divergence (regular or hidden)
Any negative divergence (regular or hidden)
Notes
The indicator may be resource-intensive due to its comprehensive calculations, especially on lower timeframes with long lookback periods.
Some features (like VWAP) can be hidden on higher timeframes to improve performance.
The default settings are optimized for daily charts but can be adjusted for any timeframe.
This powerful all-in-one indicator provides traders with a complete toolkit for technical analysis, combining trend-following, momentum, volume, and divergence techniques into a single, customizable solution.
