התחל

חפש סקריפטים עבור "12月4号是什么星座"

PubLibTrendLibrary "PubLibTrend" trend, multi-part trend, double trend and multi-part double trend conditions for indicator and strategy development rlut()   return line uptrend condition   Returns: bool dt()   downtrend condition   Returns: bool ut()   uptrend condition   Returns: bool rldt()   return line downtrend condition   Returns: bool dtop()   double top condition   Returns: bool dbot()   double bottom condition   Returns: bool rlut_1p()   1-part return line uptrend condition   Returns: bool rlut_2p()   2-part return line uptrend condition   Returns: bool rlut_3p()   3-part return line uptrend condition   Returns: bool rlut_4p()   4-part return line uptrend condition   Returns: bool rlut_5p()   5-part return line uptrend condition   Returns: bool rlut_6p()   6-part return line uptrend condition   Returns: bool rlut_7p()   7-part return line uptrend condition   Returns: bool rlut_8p()   8-part return line uptrend condition   Returns: bool rlut_9p()   9-part return line uptrend condition   Returns: bool rlut_10p()   10-part return line uptrend condition   Returns: bool rlut_11p()   11-part return line uptrend condition   Returns: bool rlut_12p()   12-part return line uptrend condition   Returns: bool rlut_13p()   13-part return line uptrend condition   Returns: bool rlut_14p()   14-part return line uptrend condition   Returns: bool rlut_15p()   15-part return line uptrend condition   Returns: bool rlut_16p()   16-part return line uptrend condition   Returns: bool rlut_17p()   17-part return line uptrend condition   Returns: bool rlut_18p()   18-part return line uptrend condition   Returns: bool rlut_19p()   19-part return line uptrend condition   Returns: bool rlut_20p()   20-part return line uptrend condition   Returns: bool rlut_21p()   21-part return line uptrend condition   Returns: bool rlut_22p()   22-part return line uptrend condition   Returns: bool rlut_23p()   23-part return line uptrend condition   Returns: bool rlut_24p()   24-part return line uptrend condition   Returns: bool rlut_25p()   25-part return line uptrend condition   Returns: bool rlut_26p()   26-part return line uptrend condition   Returns: bool rlut_27p()   27-part return line uptrend condition   Returns: bool rlut_28p()   28-part return line uptrend condition   Returns: bool rlut_29p()   29-part return line uptrend condition   Returns: bool rlut_30p()   30-part return line uptrend condition   Returns: bool dt_1p()   1-part downtrend condition   Returns: bool dt_2p()   2-part downtrend condition   Returns: bool dt_3p()   3-part downtrend condition   Returns: bool dt_4p()   4-part downtrend condition   Returns: bool dt_5p()   5-part downtrend condition   Returns: bool dt_6p()   6-part downtrend condition   Returns: bool dt_7p()   7-part downtrend condition   Returns: bool dt_8p()   8-part downtrend condition   Returns: bool dt_9p()   9-part downtrend condition   Returns: bool dt_10p()   10-part downtrend condition   Returns: bool dt_11p()   11-part downtrend condition   Returns: bool dt_12p()   12-part downtrend condition   Returns: bool dt_13p()   13-part downtrend condition   Returns: bool dt_14p()   14-part downtrend condition   Returns: bool dt_15p()   15-part downtrend condition   Returns: bool dt_16p()   16-part downtrend condition   Returns: bool dt_17p()   17-part downtrend condition   Returns: bool dt_18p()   18-part downtrend condition   Returns: bool dt_19p()   19-part downtrend condition   Returns: bool dt_20p()   20-part downtrend condition   Returns: bool dt_21p()   21-part downtrend condition   Returns: bool dt_22p()   22-part downtrend condition   Returns: bool dt_23p()   23-part downtrend condition   Returns: bool dt_24p()   24-part downtrend condition   Returns: bool dt_25p()   25-part downtrend condition   Returns: bool dt_26p()   26-part downtrend condition   Returns: bool dt_27p()   27-part downtrend condition   Returns: bool dt_28p()   28-part downtrend condition   Returns: bool dt_29p()   29-part downtrend condition   Returns: bool dt_30p()   30-part downtrend condition   Returns: bool ut_1p()   1-part uptrend condition   Returns: bool ut_2p()   2-part uptrend condition   Returns: bool ut_3p()   3-part uptrend condition   Returns: bool ut_4p()   4-part uptrend condition   Returns: bool ut_5p()   5-part uptrend condition   Returns: bool ut_6p()   6-part uptrend condition   Returns: bool ut_7p()   7-part uptrend condition   Returns: bool ut_8p()   8-part uptrend condition   Returns: bool ut_9p()   9-part uptrend condition   Returns: bool ut_10p()   10-part uptrend condition   Returns: bool ut_11p()   11-part uptrend condition   Returns: bool ut_12p()   12-part uptrend condition   Returns: bool ut_13p()   13-part uptrend condition   Returns: bool ut_14p()   14-part uptrend condition   Returns: bool ut_15p()   15-part uptrend condition   Returns: bool ut_16p()   16-part uptrend condition   Returns: bool ut_17p()   17-part uptrend condition   Returns: bool ut_18p()   18-part uptrend condition   Returns: bool ut_19p()   19-part uptrend condition   Returns: bool ut_20p()   20-part uptrend condition   Returns: bool ut_21p()   21-part uptrend condition   Returns: bool ut_22p()   22-part uptrend condition   Returns: bool ut_23p()   23-part uptrend condition   Returns: bool ut_24p()   24-part uptrend condition   Returns: bool ut_25p()   25-part uptrend condition   Returns: bool ut_26p()   26-part uptrend condition   Returns: bool ut_27p()   27-part uptrend condition   Returns: bool ut_28p()   28-part uptrend condition   Returns: bool ut_29p()   29-part uptrend condition   Returns: bool ut_30p()   30-part uptrend condition   Returns: bool rldt_1p()   1-part return line downtrend condition   Returns: bool rldt_2p()   2-part return line downtrend condition   Returns: bool rldt_3p()   3-part return line downtrend condition   Returns: bool rldt_4p()   4-part return line downtrend condition   Returns: bool rldt_5p()   5-part return line downtrend condition   Returns: bool rldt_6p()   6-part return line downtrend condition   Returns: bool rldt_7p()   7-part return line downtrend condition   Returns: bool rldt_8p()   8-part return line downtrend condition   Returns: bool rldt_9p()   9-part return line downtrend condition   Returns: bool rldt_10p()   10-part return line downtrend condition   Returns: bool rldt_11p()   11-part return line downtrend condition   Returns: bool rldt_12p()   12-part return line downtrend condition   Returns: bool rldt_13p()   13-part return line downtrend condition   Returns: bool rldt_14p()   14-part return line downtrend condition   Returns: bool rldt_15p()   15-part return line downtrend condition   Returns: bool rldt_16p()   16-part return line downtrend condition   Returns: bool rldt_17p()   17-part return line downtrend condition   Returns: bool rldt_18p()   18-part return line downtrend condition   Returns: bool rldt_19p()   19-part return line downtrend condition   Returns: bool rldt_20p()   20-part return line downtrend condition   Returns: bool rldt_21p()   21-part return line downtrend condition   Returns: bool rldt_22p()   22-part return line downtrend condition   Returns: bool rldt_23p()   23-part return line downtrend condition   Returns: bool rldt_24p()   24-part return line downtrend condition   Returns: bool rldt_25p()   25-part return line downtrend condition   Returns: bool rldt_26p()   26-part return line downtrend condition   Returns: bool rldt_27p()   27-part return line downtrend condition   Returns: bool rldt_28p()   28-part return line downtrend condition   Returns: bool rldt_29p()   29-part return line downtrend condition   Returns: bool rldt_30p()   30-part return line downtrend condition   Returns: bool dut()   double uptrend condition   Returns: bool ddt()   double downtrend condition   Returns: bool dut_1p()   1-part double uptrend condition   Returns: bool dut_2p()   2-part double uptrend condition   Returns: bool dut_3p()   3-part double uptrend condition   Returns: bool dut_4p()   4-part double uptrend condition   Returns: bool dut_5p()   5-part double uptrend condition   Returns: bool dut_6p()   6-part double uptrend condition   Returns: bool dut_7p()   7-part double uptrend condition   Returns: bool dut_8p()   8-part double uptrend condition   Returns: bool dut_9p()   9-part double uptrend condition   Returns: bool dut_10p()   10-part double uptrend condition   Returns: bool dut_11p()   11-part double uptrend condition   Returns: bool dut_12p()   12-part double uptrend condition   Returns: bool dut_13p()   13-part double uptrend condition   Returns: bool dut_14p()   14-part double uptrend condition   Returns: bool dut_15p()   15-part double uptrend condition   Returns: bool dut_16p()   16-part double uptrend condition   Returns: bool dut_17p()   17-part double uptrend condition   Returns: bool dut_18p()   18-part double uptrend condition   Returns: bool dut_19p()   19-part double uptrend condition   Returns: bool dut_20p()   20-part double uptrend condition   Returns: bool dut_21p()   21-part double uptrend condition   Returns: bool dut_22p()   22-part double uptrend condition   Returns: bool dut_23p()   23-part double uptrend condition   Returns: bool dut_24p()   24-part double uptrend condition   Returns: bool dut_25p()   25-part double uptrend condition   Returns: bool dut_26p()   26-part double uptrend condition   Returns: bool dut_27p()   27-part double uptrend condition   Returns: bool dut_28p()   28-part double uptrend condition   Returns: bool dut_29p()   29-part double uptrend condition   Returns: bool dut_30p()   30-part double uptrend condition   Returns: bool ddt_1p()   1-part double downtrend condition   Returns: bool ddt_2p()   2-part double downtrend condition   Returns: bool ddt_3p()   3-part double downtrend condition   Returns: bool ddt_4p()   4-part double downtrend condition   Returns: bool ddt_5p()   5-part double downtrend condition   Returns: bool ddt_6p()   6-part double downtrend condition   Returns: bool ddt_7p()   7-part double downtrend condition   Returns: bool ddt_8p()   8-part double downtrend condition   Returns: bool ddt_9p()   9-part double downtrend condition   Returns: bool ddt_10p()   10-part double downtrend condition   Returns: bool ddt_11p()   11-part double downtrend condition   Returns: bool ddt_12p()   12-part double downtrend condition   Returns: bool ddt_13p()   13-part double downtrend condition   Returns: bool ddt_14p()   14-part double downtrend condition   Returns: bool ddt_15p()   15-part double downtrend condition   Returns: bool ddt_16p()   16-part double downtrend condition   Returns: bool ddt_17p()   17-part double downtrend condition   Returns: bool ddt_18p()   18-part double downtrend condition   Returns: bool ddt_19p()   19-part double downtrend condition   Returns: bool ddt_20p()   20-part double downtrend condition   Returns: bool ddt_21p()   21-part double downtrend condition   Returns: bool ddt_22p()   22-part double downtrend condition   Returns: bool ddt_23p()   23-part double downtrend condition   Returns: bool ddt_24p()   24-part double downtrend condition   Returns: bool ddt_25p()   25-part double downtrend condition   Returns: bool ddt_26p()   26-part double downtrend condition   Returns: bool ddt_27p()   27-part double downtrend condition   Returns: bool ddt_28p()   28-part double downtrend condition   Returns: bool ddt_29p()   29-part double downtrend condition   Returns: bool ddt_30p()   30-part double downtrend condition   Returns: bool
ספריית Pine Script®
מאת ‎theEccentricTrader‎
2
Rise Sense Capital - RSI MACD Spot Buying IndicatorToday, I'll share a spot buying strategy shared by a member @KR陳 within the DATA Trader Alliance Alpha group. First, you need to prepare two indicators: 今天分享一個DATA交易者聯盟Alpha群組裏面的群友@KR陳分享的現貨買入策略。 首先需要準備兩個指標 RSI Indicator (Relative Strength Index) - RSI is a technical analysis tool based on price movements over a period of time to evaluate the speed and magnitude of price changes. RSI calculates the changes in price over a period to determine whether the recent trend is relatively strong (bullish) or weak (bearish). RSI指標，(英文全名:Relative Strength Index)，中文稱為「相對強弱指標」，是一種以股價漲跌為基礎，在一段時間內的收盤價，用於評估價格變動的速度　 (快慢) 與變化 (幅度) 的技術分析工具，RSI藉由計算一段期間內股價的漲跌變化，判斷最近的趨勢屬於偏強 (偏多) 還是偏弱 (偏空)。 MACD Indicator (Moving Average Convergence & Divergence) - MACD is a technical analysis tool proposed by Gerald Appel in the 1970s. It is commonly used in trading to determine trend reversals by analyzing the convergence and divergence of fast and slow lines. MACD 指標 (Moving Average Convergence & Divergence) 中文名為平滑異同移動平均線指標，MACD 是在 1970 年代由美國人 Gerald Appel 所提出，是一項歷史悠久且經常在交易中被使用的技術分析工具，原理是利用快慢線的交錯，藉以判斷股價走勢的轉折。 In MACD analysis, the most commonly used values are 12, 26, and 9, known as MACD (12,26,9). The market often uses the MACD indicator to determine the future direction of assets and to identify entry and exit points. 在 MACD 的技術分析中，最常用的值為 12 天、26 天、9 天，也稱為 MACD (12,26,9)，市場常用 MACD 指標來判斷操作標的的後市走向，確定波段漲幅並找到進、出場點。 Strategy analysis by member KR陳: 策略解析 by群友 KR陳 ： Condition 1: RSI value in the previous candle is below oversold zone(30). 條件1：RSI 在前一根的數值低於超賣區（30） buycondition1 = RSI <30 Condition 2: MACD histogram changes from decreasing to increasing. 條件2：MACD柱由遞減轉遞增 buycondition2 = hist >hist and hist
אינדיקטור Pine Script®
מאת ‎kgynofomo‎
3
Absolute Momentum (Time Series Momentum)Absolute momentum , also known as time series momentum , focuses on the trend of an asset's own past performance to predict its future performance. It involves analyzing an asset's own historical performance, rather than comparing it to other assets. The strategy determines whether an asset's price is exhibiting an upward (positive momentum) or downward (negative momentum) trend by assessing the asset's return over a given period (standard look-back period: 12 months or approximately 250 trading days). Some studies recommend calculating momentum by deducting the corresponding Treasury bill rate from the measured performance. Absolute Momentum Indicator The Absolute Momentum Indicator displays the rolling 12-month performance (measured over 250 trading days) and plots it against a horizontal line representing 0%. If the indicator crosses above this line, it signifies positive absolute momentum, and conversely, crossing below indicates negative momentum. An additional, optional look-back period input field can be accessed through the settings. Hint: This indicator is a simplified version, as some academic approaches measure absolute momentum by subtracting risk-free rates from the 12-month performance. However, even with higher rates, the values will still remain close to the 0% line. Benefits of Absolute Momentum Absolute momentum, which should not be confused with relative momentum or the momentum indicator, serves as a timing instrument for both individual assets and entire markets. Gary Antonacci , a key contributor to the absolute momentum strategy (find study below), emphasizes its effectiveness in multi-asset portfolios and its importance in long-only investing. This is particularly evident in a) reducing downside volatility and b) mitigating behavioral biases. Moskowitz, Ooi, and Pedersen document significant 'time series momentum' across various asset classes, including equity index, currency, commodity, and bond futures, in 58 liquid instruments (find study below). There's a notable persistence in returns ranging from one to 12 months, which tends to partially reverse over longer periods. This pattern aligns with sentiment theories suggesting initial under-reaction followed by delayed over-reaction. Despite its surprising ease of implementation, the academic community has successfully measured the effects of absolute momentum across decades and in every major asset class, including stocks, bonds, commodities, and foreign exchange (FX). Strategies for Implementing Absolute Momentum: To Buy a Stock: Select a Look-Back Period: Choose a historical period to analyze the stock's performance. A common period is 12 months, but this can vary based on your investment strategy. Calculate Excess Return: Determine the stock's excess return over this period. You can also assume a risk-free rate of "0" to simplify the process. Evaluate Momentum: If the excess return is positive, it indicates positive absolute momentum. This suggests the stock is in an upward trend and could be a good buying opportunity. If the excess return is negative, it suggests negative momentum, and you might want to delay buying. Consider further conditions: Align your decision with broader market trends, economic indicators, or fundamental analysis, for additional context. To Sell a Stock You Own: Regularly Monitor Performance: Use the same look-back period as for buying (e.g., 12 months) to regularly assess the stock's performance. Check for Negative Momentum: Calculate the excess return for the look-back period. Again, you can assume a risk-free rate of "0" to simplify the process. If the stock shows negative momentum, it might be time to consider selling. Consider further conditions:Align your decision with broader market trends, economic indicators, or fundamental analysis, for additional context. Important note: Note: Entering a position (i.e., buying) based on positive absolute momentum doesn't necessarily mean you must sell it if it later exhibits negative absolute momentum. You can initiate a position using positive absolute momentum as an entry indicator and then continue holding it based on other criteria, such as fundamental analysis. General Tips: Reassessment Frequency: Decide how often you will reassess the momentum (monthly, quarterly, etc.). Remember, while absolute momentum provides a systematic approach, it's recommendable to consider it as part of a broader investment strategy that includes diversification, risk management, fundamental analysis, etc. Relevant Capital Market Studies: Antonacci, Gary. "Absolute momentum: A simple rule-based strategy and universal trend-following overlay." Available at SSRN 2244633 (2013) Moskowitz, Tobias J., Yao Hua Ooi, and Lasse Heje Pedersen. "Time series momentum." Journal of financial economics 104.2 (2012): 228-250
אינדיקטור Pine Script®
מאת ‎DeltaValue‎
8
S&P 500 Quandl Data & RatiosTradingView has a little-known integration that allows you to pull in 3rd party data-sets from Nasdaq Data Link, also known as Quandl. Today, I am open-sourcing for the community an indicator that uses the Quandl integration to pull in historical data and ratios on the S&P500. I originally coded this to study macro P/E ratios during peaks and troughs of boom/bust cycles. The indicator pulls in each of the following datasets, as defined and provided by Quandl. The user can select which datasets to pull in using the indicator settings: Dividend Yield : S&P 500 dividend yield (12 month dividend per share)/price. Yields following June 2022 (including the current yield) are estimated based on 12 month dividends through June 2022, as reported by S&P. Sources: Standard & Poor's for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields. Price Earning Ratio : Price to earnings ratio, based on trailing twelve month as reported earnings. Current PE is estimated from latest reported earnings and current market price. Source: Robert Shiller and his book Irrational Exuberance for historic S&P 500 PE Ratio. CAPE/Shiller PE Ratio : Shiller PE ratio for the S&P 500. Price earnings ratio is based on average inflation-adjusted earnings from the previous 10 years, known as the Cyclically Adjusted PE Ratio (CAPE Ratio), Shiller PE Ratio, or PE 10 FAQ. Data courtesy of Robert Shiller from his book, Irrational Exuberance. Earnings Yield : S&P 500 Earnings Yield. Earnings Yield = trailing 12 month earnings divided by index price (or inverse PE) Yields following March, 2022 (including current yield) are estimated based on 12 month earnings through March, 2022 the latest reported by S&P. Source: Standard & Poor's Price Book Ratio : S&P 500 price to book value ratio. Current price to book ratio is estimated based on current market price and S&P 500 book value as of March, 2022 the latest reported by S&P. Source: Standard & Poor's Price Sales Ratio : S&P 500 Price to Sales Ratio (P/S or Price to Revenue). Current price to sales ratio is estimated based on current market price and 12 month sales ending March, 2022 the latest reported by S&P. Source: Standard & Poor's Inflation Adjusted SP500 : Inflation adjusted SP500. Other than the current price, all prices are monthly average closing prices. Sources: Standard & Poor's Robert Shiller and his book Irrational Exuberance for historic S&P 500 prices, and historic CPIs. Revenue Per Share : Trailing twelve month S&P 500 Sales Per Share (S&P 500 Revenue Per Share) non-inflation adjusted current dollars. Source: Standard & Poor's Earnings Per Share : S&P 500 Earnings Per Share. 12-month real earnings per share inflation adjusted, constant August, 2022 dollars. Sources: Standard & Poor's for current S&P 500 Earnings. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Earnings. Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
אינדיקטור Pine Script®
מאת ‎TradeAutomation‎
7
Argo II - (alerts for 3commas composite bots) - publicThis script lets users create BUY/SELL alerts for 3commas composite bots (1 alert = 12 pairs) in a simple way, based on a built in set of indicators that can be tweaked to work together or alone through the study settings. There is a version of this script for single pair bots, with slightly more features here . If the user choses to create both BUY and SELL signals from the study settings, the (1) alert created will send both BUY and SELL signals for all 12 pairs selected. At this stage, the script forces the user to select 12 pairs in the study settings. If less pairs are inserted, it will not work. Also, the script will only send alerts for the pairs selected in the study settings, not for the current chart (if different). How to use: - Add the script to the current chart - Open the study settings , insert bot details and select 12 pairs. You should write the pairs manually, using the format BTC , ADA, ETH, etc. They MUST be in capital letters or 3commas will not recognize them. - Still in the study settings, tweak the deal start/close conditions from various indicators until happy. The study will plot the entry / exit points below the current chart (1 = buy, 2 = sell) - Make sure your strategy works for all the pairs you have selected, simply by checking each chart with the same study settings - When happy, right click on the "..." next to the study name, then "Add alert'". - Under "Condition", on the second line, chose "Any alert () function call". Add the webhook from 3commas, give it a name, and "create". That's it. Notes: - If you insert coins that are not available for the quote currency and exchange of your choosing, the script will not work and return an error. - Make sure you run tests with paper trading or dummy bots (i.e without actual bot ID) to ensure your alerts trigger as intended on all coins. - If alerts trigger too much (i.e they all trigger at the same time for all coins), Trading View will stop the alert. So probably not ideal for a scalping bot. It could also be the sign the script doesn't work as intended. - The script is a bit slow on my side. I am a beginner in pinescript, so if anyone knows how to simplify it, please let me know. - if anyone knows how to tell the script to function with less than 12 pairs (when not filling the 12 fields in the setting), please also let me know :)
אינדיקטור Pine Script®
מאת ‎Galactus-B‎
16
XPloRR MA-Buy ATR-Trailing-Stop Long Term Strategy Beating B&HXPloRR MA-Buy ATR-MA-Trailing-Stop Strategy Long term MA Trailing Stop strategy to beat Buy&Hold strategy None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy. Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade. My buy strategy is triggered by the EMA(blue) crossing over the SMA curve(orange). My sell strategy is triggered by another EMA(lime) of the close value crossing the trailing stop(green) value. The trailing stop value(green) is set to a multiple of the ATR(15) value. ATR(15) is the SMA(15) value of the difference between high and low values. Every stock has it's own "DNA", so first thing to do is find the right parameters to get the best strategy values voor EMA, SMA and Trailing Stop. Then keep using these parameter for future buy/sell signals only for that particular stock. Do the same for other stocks. Here are the parameters: Exponential MA: buy trigger when crossing over the SMA value (use values between 11-50) Simple MA: buy trigger when EMA crosses over the SMA value (use values between 20 and 200) Stop EMA: sell trigger when Stop EMA of close value crosses under the trailing stop value (use values between 8 and 16) Trailing Stop #ATR: defines the trailing stop value as a multiple of the ATR(15) value Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now): BAR(Barco): EMA=11, SMA=82, StopEMA=12, Stop#ATR=9 Buy&HoldProfit: 45.82%, NetProfit: 294.7%, #Trades:8, %Profit:62.5%, ProfitFactor: 12.539 AAPL(Apple): EMA=12, SMA=45, StopEMA=12, Stop#ATR=6 Buy&HoldProfit: 2925.86%, NetProfit: 4035.92%, #Trades:10, %Profit:60%, ProfitFactor: 6.36 BEKB(Bekaert): EMA=12, SMA=42, StopEMA=12, Stop#ATR=7 Buy&HoldProfit: 81.11%, NetProfit: 521.37%, #Trades:10, %Profit:60%, ProfitFactor: 2.617 SOLB(Solvay): EMA=12, SMA=63, StopEMA=11, Stop#ATR=8 Buy&HoldProfit: 43.61%, NetProfit: 151.4%, #Trades:8, %Profit:75%, ProfitFactor: 3.794 PHIA(Philips): EMA=11, SMA=80, StopEMA=8, Stop#ATR=10 Buy&HoldProfit: 56.79%, NetProfit: 198.46%, #Trades:6, %Profit:83.33%, ProfitFactor: 23.07 I am very curious to see the parameters for your stocks and please make suggestions to improve this strategy.
אסטרטגיית Pine Script®
מאת ‎XPl0RR‎
1
Dskyz (DAFE) Quantum Sentiment Flux - Beginners Dskyz (DAFE) Quantum Sentiment Flux - Beginners: Welcome to the Dskyz (DAFE) Quantum Sentiment Flux - Beginners , a strategy and concept that’s your ultimate wingman for trading futures like MNQ, NQ, MES, and ES. This gem combines lightning-fast momentum signals, market sentiment smarts, and bulletproof risk management into a system so intuitive, even newbies can trade like pros. With clean DAFE visuals, preset modes for every vibe, and a revamped dashboard that’s basically a market GPS, this strategy makes futures trading feel like a high-octane sci-fi mission. Built on the Dskyz (DAFE) legacy of Aurora Divergence, the Quantum Sentiment Flux is designed to empower beginners while giving seasoned traders a lean, sentiment-driven edge. It uses fast/slow EMA crossovers for entries, filters trades with VIX, SPX trends, and sector breadth, and keeps your account safe with adaptive stops and cooldowns. Tuned for more action with faster signals and a slick bottom-left dashboard, this updated version is ready to light up your charts and outsmart institutional traps. Let’s dive into why this strat’s a must-have and break down its brilliance. Why Traders Need This Strategy Futures markets are a wild ride—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional games that can wreck unprepared traders. Beginners often get lost in complex systems or burned by impulsive trades. The Quantum Sentiment Flux is the antidote, offering: Dead-Simple Setup: Preset modes (Aggressive, Balanced, Conservative) auto-tune signals, risk, and sizing, so you can trade without a quant degree. Sentiment Superpower: VIX filter, SPX trend, and sector breadth visuals keep you aligned with market health, dodging chop and riding trends. Ironclad Safety: Tighter ATR-based stops, 2:1 take-profits, and preset cooldowns protect your capital, even in chaotic sessions. Next-Level Visuals: Green/red entry triangles, vibrant EMAs, a sector breadth background, and a beefed-up dashboard make signals and context pop. DAFE Swagger: The clean aesthetics, sleek dashboard—ties it to Dskyz’s elite brand, making your charts a work of art. Traders need this because it’s a plug-and-play system that blends beginner-friendly simplicity with pro-level market awareness. Whether you’re just starting or scalping 5min MNQ, this strat’s your key to trading with confidence and style. Strategy Components 1. Core Signal Logic (High-Speed Momentum) The strategy’s engine is a momentum-based system using fast and slow Exponential Moving Averages (EMAs), now tuned for faster, more frequent trades. How It Works: Fast/Slow EMAs: Fast EMA (Aggressive: 5, Balanced: 7, Conservative: 9 bars) and slow EMA (12/14/18 bars) track short-term vs. longer-term momentum. Crossover Signals: Buy: Fast EMA crosses above slow EMA, and trend_dir = 1 (fast EMA > slow EMA + ATR * strength threshold). Sell: Fast EMA crosses below slow EMA, and trend_dir = -1 (fast EMA < slow EMA - ATR * strength threshold). Strength Filter: ma_strength = fast EMA - slow EMA must exceed an ATR-scaled threshold (Aggressive: 0.15, Balanced: 0.18, Conservative: 0.25) for robust signals. Trend Direction: trend_dir confirms momentum, filtering out weak crossovers in choppy markets. Evolution: Faster EMAs (down from 7–10/21–50) catch short-term trends, perfect for active futures markets. Lower strength thresholds (0.15–0.25 vs. 0.3–0.5) make signals more sensitive, boosting trade frequency without sacrificing quality. Preset tuning ensures beginners get optimized settings, while pros can tweak via mode selection. 2. Market Sentiment Filters The strategy leans hard into market sentiment with a VIX filter, SPX trend analysis, and sector breadth visuals, keeping trades aligned with the big picture. VIX Filter: Logic: Blocks long entries if VIX > threshold (default: 20, can_long = vix_close < vix_limit). Shorts are always allowed (can_short = true). Impact: Prevents longs during high-fear markets (e.g., VIX spikes in crashes), while allowing shorts to capitalize on downturns. SPX Trend Filter: Logic: Compares S&P 500 (SPX) close to its SMA (Aggressive: 5, Balanced: 8, Conservative: 12 bars). spx_trend = 1 (UP) if close > SMA, -1 (DOWN) if < SMA, 0 (FLAT) if neutral. Impact: Provides dashboard context, encouraging trades that align with market direction (e.g., longs in UP trend). Sector Breadth (Visual): Logic: Tracks 10 sector ETFs (XLK, XLF, XLE, etc.) vs. their SMAs (same lengths as SPX). Each sector scores +1 (bullish), -1 (bearish), or 0 (neutral), summed as breadth (-10 to +10). Display: Green background if breadth > 4, red if breadth < -4, else neutral. Dashboard shows sector trends (↑/↓/-). Impact: Faster SMA lengths make breadth more responsive, reflecting sector rotations (e.g., tech surging, energy lagging). Why It’s Brilliant: - VIX filter adds pro-level volatility awareness, saving beginners from panic-driven losses. - SPX and sector breadth give a 360° view of market health, boosting signal confidence (e.g., green BG + buy signal = high-probability trade). - Shorter SMAs make sentiment visuals react faster, perfect for 5min charts. 3. Risk Management The risk controls are a fortress, now tighter and more dynamic to support frequent trading while keeping accounts safe. Preset-Based Risk: Aggressive: Fast EMAs (5/12), tight stops (1.1x ATR), 1-bar cooldown. High trade frequency, higher risk. Balanced: EMAs (7/14), 1.2x ATR stops, 1-bar cooldown. Versatile for most traders. Conservative: EMAs (9/18), 1.3x ATR stops, 2-bar cooldown. Safer, fewer trades. Impact: Auto-scales risk to match style, making it foolproof for beginners. Adaptive Stops and Take-Profits: Logic: Stops = entry ± ATR * atr_mult (1.1–1.3x, down from 1.2–2.0x). Take-profits = entry ± ATR * take_mult (2x stop distance, 2:1 reward/risk). Longs: stop below entry, TP above; shorts: vice versa. Impact: Tighter stops increase trade turnover while maintaining solid risk/reward, adapting to volatility. Trade Cooldown: Logic: Preset-driven (Aggressive/Balanced: 1 bar, Conservative: 2 bars vs. old user-input 2). Ensures bar_index - last_trade_bar >= cooldown. Impact: Faster cooldowns (especially Aggressive/Balanced) allow more trades, balanced by VIX and strength filters. Contract Sizing: Logic: User sets contracts (default: 1, max: 10), no preset cap (unlike old 7/5/3 suggestion). Impact: Flexible but risks over-leverage; beginners should stick to low contracts. Built To Be Reliable and Consistent: - Tighter stops and faster cooldowns make it a high-octane system without blowing up accounts. - Preset-driven risk removes guesswork, letting newbies trade confidently. - 2:1 TPs ensure profitable trades outweigh losses, even in volatile sessions like April 27, 2025 ES slippage. 4. Trade Entry and Exit Logic The entry/exit rules are simple yet razor-sharp, now with VIX filtering and faster signals: Entry Conditions: Long Entry: buy_signal (fast EMA crosses above slow EMA, trend_dir = 1), no position (strategy.position_size = 0), cooldown passed (can_trade), and VIX < 20 (can_long). Enters with user-defined contracts. Short Entry: sell_signal (fast EMA crosses below slow EMA, trend_dir = -1), no position, cooldown passed, can_short (always true). Logic: Tracks last_entry_bar for visuals, last_trade_bar for cooldowns. Exit Conditions: Stop-Loss/Take-Profit: ATR-based stops (1.1–1.3x) and TPs (2x stop distance). Longs exit if price hits stop (below) or TP (above); shorts vice versa. No Other Exits: Keeps it straightforward, relying on stops/TPs. 5. DAFE Visuals The visuals are pure DAFE magic, blending clean function with informative metrics utilized by professionals, now enhanced by faster signals and a responsive breadth background: EMA Plots: Display: Fast EMA (blue, 2px), slow EMA (orange, 2px), using faster lengths (5–9/12–18). Purpose: Highlights momentum shifts, with crossovers signaling entries. Sector Breadth Background: Display: Green (90% transparent) if breadth > 4, red (90%) if breadth < -4, else neutral. Purpose: Faster breadth_sma_len (5–12 vs. 10–50) reflects sector shifts in real-time, reinforcing signal strength. - Visuals are intuitive, turning complex signals into clear buy/sell cues. - Faster breadth background reacts to market rotations (e.g., tech vs. energy), giving a pro-level edge. 6. Sector Breadth Dashboard The new bottom-left dashboard is a game-changer, a 3x16 table (black/gray theme) that’s your market command center: Metrics: VIX: Current VIX (red if > 20, gray if not). SPX: Trend as “UP” (green), “DOWN” (red), or “FLAT” (gray). Trade Longs: “OK” (green) if VIX < 20, “BLOCK” (red) if not. Sector Breadth: 10 sectors (Tech, Financial, etc.) with trend arrows (↑ green, ↓ red, - gray). Placeholder Row: Empty for future metrics (e.g., ATR, breadth score). Purpose: Consolidates regime, volatility, market trend, and sector data, making decisions a breeze. - VIX and SPX metrics add context, helping beginners avoid bad trades (e.g., no longs if “BLOCK”). Sector arrows show market health at a glance, like a cheat code for sentiment. Key Features Beginner-Ready: Preset modes and clear visuals make futures trading a breeze. Sentiment-Driven: VIX filter, SPX trend, and sector breadth keep you in sync with the market. High-Frequency: Faster EMAs, tighter stops, and short cooldowns boost trade volume. Safe and Smart: Adaptive stops/TPs and cooldowns protect capital while maximizing wins. Visual Mastery: DAFE’s clean flair, EMAs, dashboard—makes trading fun and clear. Backtestable: Lean code and fixed qty ensure accurate historical testing. How to Use Add to Chart: Load on a 5min MNQ/ES chart in TradingView. Pick Preset: Aggressive (scalping), Balanced (versatile), or Conservative (safe). Balanced is default. Set Contracts: Default 1, max 10. Stick low for safety. Check Dashboard: Bottom-left shows preset, VIX, SPX, and sectors. “OK” + green breadth = strong buy. Backtest: Run in strategy tester to compare modes. Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues). Replay Test: Try April 28, 2025 NQ drop to see VIX filter and stops in action. Why It’s Brilliant The Dskyz (DAFE) Quantum Sentiment Flux - Beginners is a masterpiece of simplicity and power. It takes pro-level tools—momentum, VIX, sector breadth—and wraps them in a system anyone can run. Faster signals and tighter stops make it a trading machine, while the VIX filter and dashboard keep you ahead of market chaos. The DAFE visuals and bottom-left command center turn your chart into a futuristic cockpit, guiding you through every trade. For beginners, it’s a safe entry to futures; for pros, it’s a scalping beast with sentiment smarts. This strat doesn’t just trade—it transforms how you see the market. Final Notes This is more than a strategy—it’s your launchpad to mastering futures with Dskyz (DAFE) flair. The Quantum Sentiment Flux blends accessibility, speed, and market savvy to help you outsmart the game. Load it, watch those triangles glow, and let’s make the markets your canvas! Official Statement from Pine Script Team (see TradingView help docs and forums): "This warning may appear when you call functions such as ta.sma inside a request.security in a loop. There is no runtime impact. If you need to loop through a dynamic list of tickers, this cannot be avoided in the present version... Values will still be correct. Ignore this warning in such contexts." (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
אסטרטגיית Pine Script®
מאת ‎DskyzInvestments‎
1
VIX bottom/top with color scale [Ox_kali]📊 Introduction ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The “VIX Bottom/Top with Color Scale” script is designed to provide an intuitive, color-coded visualization of the VIX (Volatility Index), helping traders interpret market sentiment and volatility extremes in real time. It segments the VIX into clear threshold zones, each associated with a specific market condition—ranging from fear to calm—using a dynamic color-coded system. This script offers significant value for the following reasons: Intuitive Risk Interpretation: Color-coded zones make it easy to interpret market sentiment at a glance. Dynamic Trend Detection: A 200-period SMA of the VIX is plotted and dynamically colored based on trend direction. Customization and Flexibility: All colors are editable in the parameters panel, grouped under “## Color parameters ##”. Visual Clarity: Key thresholds are marked with horizontal lines for quick reference. Practical Trading Tool: Helps identify high-risk and low-risk environments based on volatility levels. 🔍 Key Indicators ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ VIX (CBOE Volatility Index) : Measures market volatility and investor fear. SMA 200 : Long-term trendline of the VIX, with color-coded direction (green = uptrend, red = downtrend). Color-coded VIX Levels: 🔴 33+ → Something bad just happened 🟠 23–33 → Something bad is happening 🟡 17–23 → Something bad might happen 🟢 14–17 → Nothing bad is happening ✅ 12–14 → Nothing bad will ever happen 🔵 <12 → Something bad is going to happen 🧠 Originality and Purpose ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Unlike traditional VIX indicators that only plot a line, this script enhances interpretation through visual segmentation and dynamic trend tracking. It serves as a risk-awareness tool that transforms the VIX into a simple, emotional market map. This is the first version of the script, and future updates may include alerts, background fills, and more advanced features. ⚙️ How It Works ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The script maps the current VIX value to a range and applies the corresponding color. It calculates a SMA 200 and colors it green or red depending on its slope. It displays horizontal dotted lines at key thresholds (12, 14, 17, 23, 33). All colors are configurable via input parameters under the group: "## Color parameters ##". 🧭 Indicator Visualization and Interpretation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The VIX line changes color based on market condition zones. The SMA line shows long-term direction with dynamic color. Horizontal threshold lines visually mark the transitions between volatility zones. Ideal for quickly identifying periods of fear, caution, or stability. 🛠️ Script Parameters ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Grouped under “## Color parameters ##”, the following elements are customizable: 🎨 VIX Zone Colors: 33+ → Red 23–33 → Orange 17–23 → Yellow 14–17 → Light Green 12–14 → Dark Green <12 → Blue 📈 SMA Colors: Uptrend → Green Downtrend → Red These settings allow users to match the script’s visuals to their preferred chart style or theme. ✅ Conclusion ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ The “VIX Bottom/Top with Color Scale” is a clean, powerful script designed to simplify how traders view volatility. By combining long-term trend data with real-time color-coded sentiment analysis, this script becomes a go-to reference for managing risk, timing trades, or simply staying in tune with market mood. 🧪 Notes ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ This is version 1 of the script. More features such as alert conditions, background fill, and dashboard elements may be added soon. Feedback is welcome! 💡 Color code concept inspired by the original VIX interpretation chart by @nsquaredvalue on Twitter. Big thanks for the visual clarity! 💡 ⚠️ Disclaimer ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ This script is a visual tool designed to assist in market analysis. It does not guarantee future performance and should be used in conjunction with proper risk management. Past performance is not indicative of future results.
אינדיקטור Pine Script®
מאת ‎Ox_kali‎
1
AEST High-Low MarkerOverview This TradingView indicator, AEST High-Low Marker, is designed to mark the highest and lowest price levels observed between 5:00 PM and 6:00 PM AEST and extend these levels visually on the chart only between 5:00 PM and 12:00 AM AEST. Functionality Time Conversion for AEST Since TradingView operates in UTC, the script translates AEST (UTC+10 or UTC+11 during daylight savings) into UTC time. The script starts tracking from 5:00 PM AEST (7 AM UTC) to 6:00 PM AEST (8 AM UTC). The high and low lines will be displayed only between 5:00 PM and 12:00 AM AEST (7 AM to 2 PM UTC). Real-Time High & Low Calculation The indicator dynamically updates the session high and low as new candles form during the 5 PM - 6 PM AEST period. It captures the maximum high and minimum low during this timeframe. Line Display Restrictions The session high and low lines will only be drawn between 5:00 PM and 12:00 AM AEST to prevent chart clutter. The lines disappear after 12:00 AM AEST. Visual Representation Blue Line: Marks the session high recorded between 5 PM - 6 PM AEST. Red Line: Marks the session low recorded between 5 PM - 6 PM AEST. Both lines extend until 12 AM AEST and then disappear. Use Case This indicator is useful for traders looking to track key price levels formed between 5 PM and 6 PM AEST and observe how price interacts with these levels until midnight. It is particularly beneficial for intraday and short-term trading strategies, allowing users to identify potential support and resistance zones based on early evening price action.
אינדיקטור Pine Script®
מאת ‎LukeHouls83‎
Multi-Timeframe VWAP DashboardMulti-Timeframe VWAP Dashboard with Advanced Customization** Unlock the power of **Volume-Weighted Average Price (VWAP)** across multiple timeframes with this highly customizable and feature-rich Pine Script. Designed for traders who demand precision and flexibility, this script provides a **comprehensive VWAP dashboard** that adapts to your trading style and strategy. Whether you're a day trader, swing trader, or long-term investor, this tool offers unparalleled insights into market trends and price levels. --- ### **Key Features:** 1. **Multi-Timeframe VWAP Calculation:** - Calculate VWAP across **12-minute, 48-minute, 96-minute, 192-minute, daily, weekly, monthly, and even yearly timeframes**. - Supports **custom timeframes** for tailored analysis. 2. **Price Source Selection:** - Choose from multiple price sources for VWAP calculation, including **Open, High, Low, Close, HL2, HLC3, HLCC4, and All**. - Optimize VWAP for **uptrends and downtrends** by selecting the most relevant price source. 3. **Customizable Labels:** - Add **dynamic labels** to each VWAP line for quick reference. - Customize label **colors, sizes, and offsets** to suit your chart setup. - Display **price values** and **session types** (e.g., "12 Min", "Daily", "Weekly") directly on the chart. 4. **Advanced Session Detection:** - Automatically detect new sessions for **intraday, daily, weekly, monthly, and yearly timeframes**. - Ensures accurate VWAP calculations for each session. 5. **Plot Visibility Control:** - Toggle the visibility of individual VWAP plots to **reduce clutter** and focus on the most relevant timeframes. - Includes options for **short-term, medium-term, and long-term VWAPs**. 6. **Comprehensive Timeframe Coverage:** - From **12-minute intervals** to **12-month intervals**, this script covers all major timeframes. - Perfect for traders who analyze markets across multiple horizons. 7. **User-Friendly Inputs:** - Intuitive input options for **timeframes, colors, labels, and offsets**. - Easily customize the script to match your trading preferences. 8. **Dynamic Label Positioning:** - Labels adjust automatically based on price movements and session changes. - Choose from **multiple offset options** to position labels precisely. 9. **Miscellaneous Customization:** - Adjust **text color, label size, and price display settings**. - Enable or disable **price values** and **session type labels** for a cleaner chart. --- ### **Why Use This Script?** - **Versatility:** Suitable for all trading styles, including scalping, day trading, swing trading, and long-term investing. - **Precision:** Accurate VWAP calculations across multiple timeframes ensure you never miss key price levels. - **Customization:** Tailor the script to your specific needs with a wide range of input options. - **Clarity:** Dynamic labels and customizable plots make it easy to interpret market trends at a glance. --- ### **How It Works:** 1. **Select Your Price Source:** - Choose the price source (e.g., Open, Close, HL2) for VWAP calculation based on your trading strategy. 2. **Choose Timeframes:** - Define the timeframes for VWAP calculation, from intraday to yearly intervals. 3. **Customize Labels and Plots:** - Enable or disable labels and plots for each timeframe. - Adjust colors, sizes, and offsets to match your chart setup. 4. **Analyze Market Trends:** - Use the VWAP lines and labels to identify **support/resistance levels**, **trend direction**, and **potential reversal points**. 5. **Adapt to Market Conditions:** - Switch between timeframes and price sources to adapt to changing market conditions. --- ### **Ideal For:** - **Day Traders:** Use short-term VWAPs (e.g., 12-minute, 48-minute) to identify intraday trends and key levels. - **Swing Traders:** Leverage medium-term VWAPs (e.g., 96-minute, daily) to spot swing opportunities. - **Long-Term Investors:** Analyze long-term VWAPs (e.g., weekly, monthly) to gauge overall market direction. --- ### **How to Get Started:** 1. Add the script to your TradingView chart. 2. Customize the inputs to match your trading preferences. 3. Analyze the VWAP lines and labels to make informed trading decisions. --- ### **Pro Tip:** Combine this script with other technical indicators (e.g., moving averages, RSI) for a **holistic view** of the market. Use the VWAP lines as dynamic support/resistance levels to enhance your entry and exit strategies. This script is a must-have tool for traders who value precision, flexibility, and clarity. Share it with your audience to help them elevate their trading game. Whether they're beginners or seasoned professionals, this **Multi-Timeframe VWAP Dashboard** will become an essential part of their toolkit.
אינדיקטור Pine Script®
מאת ‎Dshergill‎
2
Anchored Geometric Brownian Motion Projections w/EVAnchored GBM (Geometric Brownian Motion) Projections + EV & Confidence Bands Version: Pine Script v6 Overlay: Yes Author: Published On: Overview The Anchored GBM Projections + EV & Confidence Bands indicator leverages the Geometric Brownian Motion (GBM) model to project future price movements based on historical data. By simulating multiple potential future price paths, it provides traders with insights into possible price trajectories, their expected values, and confidence intervals. Additionally, it offers a "Mean of EV" (EV of EV) line, representing the running average of expected values across the projection period. Key Features Anchor Time Setup: Define a specific point in time from which the projections commence. By default, it uses the current bar's timestamp but can be customized. Projection Parameters: Projection Candles (Bars): Determines the number of future bars (time periods) to project. Number of Simulations: Specifies how many GBM paths to simulate, ensuring statistical relevance via the Central Limit Theorem (CLT). Display Toggles: Simulation Lines: Visual representation of individual GBM simulation paths. Expected Value (EV) Line: The average price across all simulations at each projection bar. Upper & Lower Confidence Bands: 95% confidence intervals indicating potential price boundaries. EV of EV Line: Running average of EV values, providing a smoothed central tendency across the projection period. Additionally, this line often acts as an indicator of trend direction. Visualization: Clear and distinguishable lines with customizable colors and styles. Overlayed on the price chart for direct comparison with actual price movements. Mathematical Foundation Geometric Brownian Motion (GBM): Definition: GBM is a continuous-time stochastic process used to model stock prices. It assumes that the logarithm of the stock price follows a Brownian motion with drift. Equation: S(t)=S0⋅e(μ−12σ2)t+σW(t) S(t)=S0​⋅e(μ−21​σ2)t+σW(t) Where: S(t)S(t) = Stock price at time tt S0S0​ = Initial stock price μμ = Drift coefficient (average return) σσ = Volatility coefficient (standard deviation of returns) W(t)W(t) = Wiener process (standard Brownian motion) Drift (μμ) and Volatility (σσ): Drift (μμ) represents the expected return of the stock. Volatility (σσ) measures the stock's price fluctuation intensity. Central Limit Theorem (CLT): Principle: With a sufficiently large number of independent simulations, the distribution of the sample mean (EV) approaches a normal distribution, regardless of the underlying distribution. Application: Ensures that the EV and confidence bands are statistically reliable. Expected Value (EV) and Confidence Bands: EV: The mean price across all simulations at each projection bar. Confidence Bands: Range within which the actual price is expected to lie with a specified probability (e.g., 95%). EV of EV (Mean of Sample Means): Definition: Represents the running average of EV values across the projection period, offering a smoothed central tendency. Methodology Anchor Time Setup: The indicator starts projecting from a user-defined Anchor Time. If not customized, it defaults to the current bar's timestamp. Purpose: Allows users to analyze projections from a specific historical point or the latest market data. Calculating Drift and Volatility: Returns Calculation: Computes the logarithmic returns from the Anchor Time to the current bar. returns=ln⁡(StSt−1) returns=ln(St−1​St​​) Drift (μμ): Calculated as the simple moving average (SMA) of returns over the period since the Anchor Time. Volatility (σσ): Determined using the standard deviation (stdev) of returns over the same period. Simulation Generation: Number of Simulations: The user defines how many GBM paths to simulate (e.g., 30). Projection Candles: Determines the number of future bars to project (e.g., 12). Process: For each simulation: Start from the current close price. For each projection bar: Generate a random number zz from a standard normal distribution. Calculate the next price using the GBM formula: St+1=St⋅e(μ−12σ2)+σz St+1​=St​⋅e(μ−21​σ2)+σz Store the projected price in an array. Expected Value (EV) and Confidence Bands Calculation: EV Path: At each projection bar, compute the mean of all simulated prices. Variance and Standard Deviation: Calculate the variance and standard deviation of simulated prices to determine the confidence intervals. Confidence Bands: Using the standard normal z-score (1.96 for 95% confidence), establish upper and lower bounds: Upper Band=EV+z⋅σEV Upper Band=EV+z⋅σEV​ Lower Band=EV−z⋅σEV Lower Band=EV−z⋅σEV​ EV of EV (Running Average of EV Values): Calculation: For each projection bar, compute the average of all EV values up to that bar. EV of EV =1j+1∑k=0jEV EV of EV =j+11​k=0∑j​EV Visualization: Plotted as a dynamic line reflecting the evolving average EV across the projection period. Visualization Elements Simulation Lines: Appearance: Semi-transparent blue lines representing individual GBM simulation paths. Purpose: Illustrate a range of possible future price trajectories based on current drift and volatility. Expected Value (EV) Line: Appearance: Solid orange line. Purpose: Shows the average projected price at each future bar across all simulations. Confidence Bands: Upper Band: Dashed green line indicating the upper 95% confidence boundary. Lower Band: Dashed red line indicating the lower 95% confidence boundary. Purpose: Highlight the range within which the price is statistically expected to remain with 95% confidence. EV of EV Line: Appearance: Dashed purple line. Purpose: Displays the running average of EV values, providing a smoothed trend of the central tendency across the projection period. As the mean of sample means it approximates the population mean (i.e. the trend since the anchor point.) Current Price: Appearance: Semi-transparent white line. Purpose: Serves as a reference point for comparing actual price movements against projected paths. Usage Instructions Configuring User Inputs: Anchor Time: Set to a specific timestamp to start projections from a historical point or leave it as default to use the current bar's time. Projection Candles (Bars): Define the number of future bars to project (e.g., 12). Adjust based on your trading timeframe and analysis needs. Number of Simulations: Specify the number of GBM paths to simulate (e.g., 30). Higher numbers yield more accurate EV and confidence bands but may impact performance. Display Toggles: Show Simulation Lines: Toggle to display or hide individual GBM simulation paths. Show Expected Value Line: Toggle to display or hide the EV path. Show Upper Confidence Band: Toggle to display or hide the upper confidence boundary. Show Lower Confidence Band: Toggle to display or hide the lower confidence boundary. Show EV of EV Line: Toggle to display or hide the running average of EV values. Managing TradingView's Object Limits: Understanding Limits: TradingView imposes a limit on the number of graphical objects (e.g., lines) that can be rendered. High values for projection candles and simulations can quickly consume these limits. TradingView appears to only allow a total of 55 candles to be projected, so if you want to see two complete lines, you would have to set the projection length to 27: since 27 * 2 = 54 and 54 < 55. Optimizing Performance: Use Toggles: Enable only the necessary visual elements. For instance, disable simulation lines and confidence bands when focusing on the EV and EV of EV lines. You can also use the maximum projection length of 55 with the lower limit confidence band as the only line, visualizing a long horizon for your risk. Adjust Parameters: Lower the number of projection candles or simulations to stay within object limits without compromising essential insights. Interpreting the Indicator: Simulation Lines (Blue): Represent individual potential future price paths based on GBM. A wider spread indicates higher volatility. Expected Value (EV) Line (Goldenrod): Shows the mean projected price at each future bar, providing a central trend. Confidence Bands (Green & Red): Indicate the statistical range (95% confidence) within which the price is expected to remain. EV of EV Line (Dotted Line - Goldenrod): Reflects the running average of EV values, offering a smoothed perspective of expected price trends over the projection period. Current Price (White): Serves as a benchmark for assessing how actual prices compare to projected paths. Practical Applications Risk Management: Confidence Bands: Help in identifying potential support and resistance levels based on statistical confidence intervals. EV Path: Assists in setting realistic target prices and stop-loss levels aligned with projected expectations. Trend Analysis: EV of EV Line: Offers a smoothed trendline, aiding in identifying overarching market directions amidst price volatility. Indicative of the population mean/overall trend of the data since your anchor point. Scenario Planning: Simulation Lines: Enable traders to visualize multiple potential outcomes, fostering better decision-making under uncertainty. Performance Evaluation: Comparing Actual vs. Projected Prices: Assess how actual price movements align with projected scenarios, refining trading strategies over time. Mathematical and Statistical Insights Simulation Integrity: Independence: Each simulation path is generated independently, ensuring unbiased and diverse projections. Randomness: Utilizes a Gaussian random number generator to introduce variability in diffusion terms, mimicking real market randomness. Statistical Reliability: Central Limit Theorem (CLT): By simulating a sufficient number of paths (e.g., 30), the sample mean (EV) converges to the population mean, ensuring reliable EV and confidence band calculations. Variance Calculation: Accurate computation of variance from simulation data ensures precise confidence intervals. Dynamic Projections: Running Average (EV of EV): Provides a cumulative perspective, allowing traders to observe how the average expectation evolves as the projection progresses. Customization and Enhancements Adjustable Parameters: Tailor the projection length and simulation count to match your trading style and analysis depth. Visual Customization: Modify line colors, styles, and transparency to enhance clarity and fit chart aesthetics. Extended Statistical Metrics: Future iterations can incorporate additional metrics like median projections, skewness, or alternative confidence intervals. Dynamic Recalculation: Implement logic to automatically update projections as new data becomes available, ensuring real-time relevance. Performance Considerations Object Count Management: High simulation counts and extended projection periods can lead to a significant number of graphical objects, potentially slowing down chart performance. Solution: Utilize display toggles effectively and optimize projection parameters to balance detail with performance. Computational Efficiency: The script employs efficient array handling and conditional plotting to minimize unnecessary computations and object creation. Conclusion The Anchored GBM Projections + EV & Confidence Bands indicator is a robust tool for traders seeking to forecast potential future price movements using statistical models. By integrating Geometric Brownian Motion simulations with expected value calculations and confidence intervals, it offers a comprehensive view of possible market scenarios. The addition of the "EV of EV" line further enhances analytical depth by providing a running average of expected values, aiding in trend identification and strategic decision-making. Hope it helps!
אינדיקטור Pine Script®
מאת ‎garysebastianbrowniii‎
3
CSVParser█  OVERVIEW The library contains functions for parsing and importing complex CSV configurations (with a special simple syntax) into a special hierarchical object (of type objProps ) as follows: Functions: parseConfig() - reads CSV text into an objProps object. toT() - displays the contents of an objProps object in a table form, which allows to check the CSV text for syntax errors. getPropAr() - returns objProps.arS array for child object with `prop` key in mpObj map (or na if not found) This library is handy in allowing users to store presets for the scripts and switch between them (see, e.g., my HTF moving averages script where users can switch between several preset configuations of 24 MA's across 5 timeframes). █  HOW THE SCRIPT WORKS. The script works as follows: all values read from config text are stored as strings Nested brackets in config text create a named nested objects of objProps0, ... , objProps9 types. objProps objects of each level have the following fields: - array arS for storing values without names (e.g. "12, 23" will be imported into a string array arS as ) - map mpS for storing items with names (e.g. "tf = 60, length = 21" will be imported as <"tf", "60"> and <"length", "21"> pairs into mpS ) - map mpObj for storing nested objects (e.g. "TF1(tf=60, length(21,50,100))" creates a <"TF1, objProps0 object> pair in mpObj map property of the top level object (objProps) , "tf=60" is stored as <"tf", "60"> key-value pair in mpS map property of a next level object (objProps0) and "length (...)" creates a <"length", objProps1> pair in objProps0.mpObj map while length values are stored in objProps1.arS array as strings. Every opening bracket creates a next level objProps object. If objects or properties with duplicate names are encountered only the latest is imported (e.g. for "TF1(length(12,22)), TF1(tf=240)" only "TF1(tf=240)" will be imported Line breaks are not regarded as part of syntax (i.e. values are imported with line breaks, you can supply symbols "(" , ")" , "," and "=" are special characters and cannot be used within property values (with the exception of a quoted text as a value of a property as explained below) named properties can have quoted text as their value. In that case special characters within quotation marks are regarded as normal characters. Text between "=" and opening quotation mark as well as text following the closing quotation mark and until next property value is ignored. E.g. "quote = ignored "The quote" also ignored" will be imported as <"quote", "The quote">. Quotation marks within quotes must be excaped with "\" . if a key names happens to be a multi-line then only first line containing non-space characters (trimmed from spaces) is taken as a key. ")," or ") ," and similar do not create an empty ("") array item while ",," does. (",)" creates an "" array item) █  CSV CONFIGURATION SYNTAX Unnamed values: just list them comma separated and they will be imported into arS of the object of the current level. Named values: use "=" sign as follows: "property1=value1, property2 = value2" Value of several objects: Use brackets after the name of the object ant list all object properties within the brackets (including its child objects if necessary). E.g. "TF1(tf =60, length(21,200), TF2(tf=240, length(50,200)" Named and unnamed values as well as objects can go in any order. E.g. "12, tf=60, 21" will be imported as follows: "12", "21" will go to arS array and <"tf", "60"> will go to mpS maP of objProps (the top level object). You can play around and test your config text using demo in this library, just edit your text in script settings and see how it is parsed into objProps objects. █  USAGE RECOMMENDATIONS AND SAMPLE USE I suggest the following approach: - create functions for your UDT which can set properties by name. - create enumerator functions which iterates through all the property names (supplied as a const string array) and imports their values into the object █  SAMPLE USE A sample use of this library can be seen in my Multi-timeframe 24 moving averages + BB+SAR+Supertrend+VWAP script where settings for the MAs across many timeframes are imported from CSV configurations (presets). █  FULL LIST OF FUNCTIONS AND PROPERTIES nzs(_s, nz)   Like nz() but for strings. Returns `nz` arg (default = "") if _s is na.   Parameters:      _s (string)      nz (string) method init(this)   Initializes objProps obj (creates child maps and arrays)   Namespace types: objProps   Parameters:      this (objProps) method toT(this, nz)   Outputs objProps to string matrices for further display using autotable().   Namespace types: objProps, objProps1, ..., objProps9   Parameters:      this (objProps/objProps1/..../objProps9)      nz (string)   Returns: A tuple - value, merge and color matrix (autotable() parameters) method parseConfig(this, s)   Reads config text into objProps (unnamed values into arS, named into mpS, sub-levels into mpObj)   Namespace types: objProps   Parameters:      this (objProps)      s (string) method getPropArS(this, prop)   Returns a string array of values for a given property name `prop`. Looks for a key `prop` in objProps.mpObj if finds pair returns obj.arS, otherwise returns na. Returns a reference to the original, not a copy.   Namespace types: objProps, objProps1, ..., objProps8   Parameters:      this (objProps/objProps1/..../objProps8)      prop (string) method getPropVal(this, prop, id)   Checks if there is an array of values for property `prop` and returns its `id`'s element or na if not found   Namespace types: objProps, objProps1, ..., objProps8   Parameters:      this (objProps/objProps1/..../objProps8) : objProps object containing array of property values in a child objProp object corresponding to propertty name.      prop (string) : (string) Name of the property      id (int) : (int) Id of the element to be returned from the array pf property values objProps9 type   Object for storing values read from CSV relating to a particular object or property name.   Fields:      mpS (map) : (map() Stores property values as pairs      arS (array) : (string ) Array of values objProps, objProps0, ... objProps8 types   Object for storing values read from CSV relating to a particular object or property name.   Fields:      mpS (map) : (map() Stores property values as pairs      arS (array) : (string ) Array of values      mpObj (map) : (map() Stores objProps objects containing properties's data as pairs
ספריית Pine Script®
מאת ‎moebius1977‎
@tk · fractal emas█  OVERVIEW This script is an indicator that plots short, medium and long moving averages for multiple fractals. This script was based on sharks EMAs by rlvs indicator, that plots multiple rays for each fractals into the chart. The main feature of this indicator is the customizability. The calculation itself is simple as moving average. █  MOTIVATION The trader can customize all aspects of the plotted data. The text size, extended line length, the moving average type — exponential, simple, etc... — the length of fractal rays, line style, line width and visibility. To keep minimalist, this indicator simplifies the logic of line colors based on the purpose of each moving averages. To prevent overnoise the chart with multiple lines with multiple colors for each fractal timefraes, the trader needs to keep in mind that the all lines with the "short" moving average color for example, will represents the short moving averages lines for all fractals. This logic is applied for medium and long moving averages either. █  CONCEPT The trading concept to use this indicator is to make entries on uptrend or downtrend pullbacks when the asset price reaches the short, medium or long moving averages price levels. But this strategy don't works alone. It needs to be aligned together with others indicators like RSI, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis. █  CUSTOMIZATION This indicator allows the trader to customize the following settings: GENERAL Text size Changes the font size of the labels to improve accessibility. Type: string Options: `tiny`, `small`, `normal`, `large`. Default: `small` SHORT Type Select the Short Moving Average calculation type. Type: string Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`. Default: `EMA` Length Changes the base length for the Short Moving Average calculation. Type: int Default: 12 Source Changes the base source for the Short Moving Average calculation. Type: float Default: close Color The base color that will represent the Short Moving Average. Type: color Default: color.rgb(255, 235, 59) (yellow) Fractal Style The fractal ray line style. Type: string Options: `dotted`, `dashed`, `solid`. Default: `dotted` Fractal Width The fractal ray line width. Type: string Options: `1px`, `2px`, `3px`, `4px`. Default: `1px` Fractal Ray Length The fractal ray line length. Type: int Default: 12 MEDIUM Type Select the Medium Moving Average calculation type. Type: string Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`. Default: `EMA` Length Changes the base length for the Medium Moving Average calculation. Type: int Default: 26 Source Changes the base source for the Medium Moving Average calculation. Type: float Default: close Color The base color that will represent the Short Moving Average. Type: color Default: color.rgb(0, 230, 118) (lime) Fractal Style The fractal ray line style. Type: string Options: `dotted`, `dashed`, `solid`. Default: `dotted` Fractal Width The fractal ray line width. Type: string Options: `1px`, `2px`, `3px`, `4px`. Default: `1px` Fractal Ray Length The fractal ray line length. Type: int Default: 12 LONG Type Select the Long Moving Average calculation type. Type: string Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`. Default: `EMA` Length Changes the base length for the Long Moving Average calculation. Type: int Default: 200 Source Changes the base source for the Long Moving Average calculation. Type: float Default: close Color The base color that will represent the Short Moving Average. Type: color Default: color.rgb(255, 82, 82) (red) Fractal Style The fractal ray line style. Type: string Options: `dotted`, `dashed`, `solid`. Default: `dotted` Fractal Width The fractal ray line width. Type: string Options: `1px`, `2px`, `3px`, `4px`. Default: `1px` Fractal Ray Length The fractal ray line length. Type: int Default: 12 VISIBILITY Show Fractal Rays · (Short) Shows short moving average fractal rays. Type: bool Default: true Show Fractal Rays · (Medium) Shows short moving average fractal rays. Type: bool Default: true Show Fractal Rays · (Long) Shows short moving average fractal rays. Type: bool Default: true █  FUNCTIONS The script contains the following functions: `fn_labelizeTimeFrame` Labelize timeframe period in minutes and hours. Parameters: tf: (string) Timeframe period to be labelized. Returns: (string) Labelized timeframe string. `fn_builtInLineStyle` Converts simple string to built-in line style variable value. Parameters: lineStyle: (string) The line style simple string. Returns: (string) Built-in line style string value. `fn_builtInLineWidth` Converts simple pixel string to line width number value. Parameters: lineWidth: (string) The line width pixel simple string. Returns: (string) Built-in line width number value. `fn_requestFractal` Requests fractal data based on `period` given an expression. Parameters: period: (string) The period timeframe of fractal. expression: (series float) The expression to retrieve data from fractal. Returns: (mixed) A result determined by `expression`. `fn_plotRay` Plots line after chart bars. Parameters: y: (float) Y axis line position. label: (string) Label to be ploted after line. color: (color) Line and label color. length: (int) Line length. show: (bool) Flag to display the line. (default: `true`) lineStyle: (string) Line style to be applied. (default: `line.style_dotted`) lineWidth: (int) Line width. (default: `1`) Returns: void `fn_plotEmaRay` Plots moving average line for a specific period. Parameters: period: (simple string) Period of fractal to retrieve expression: (series float) The expression to retrieve data from fractal. color: (color) Line and label color. length: (int) Line length. (default: `12`) show: (bool) Flag to display the line. (default: `true`) lineStyle: (string) Line style to be applied. (default: `line.style_dotted`) lineWidth: (string) Line width. (default: `1px`) Returns: void `fn_plotExtendedEmaRay` Draws extended line for current timeframe moving average. Parameters: coordY: (float) Extended line Y axis position. textValue: (simple string) Extended line label text. textColor: (color) Extended line text color. length: (int) Extended length. (default: `5`) Returns: void
אינדיקטור Pine Script®
מאת ‎gabrielrtakeda‎
Moving Average Rainbow (Stormer)This strategy is based and shown by trader and investor Alexandre Wolwacz "Stormer". Overview The strategy uses 12 moving averages (default EMA) to identify trends and generate trading signals opening positions. Allowing to select the type of moving average and length to be used. The conditions includes relationship between moving averages, the position of the current price relative to the moving averages, and the occurrence of certain price patterns. Calculation The mean moving averages is calculated by adding all the 12 moving averages and dividing by 12, the value is used to help to identify trend and possible condition to open position. The 12 moving averages is spliced by 3 ranges, initial range (moving average lines 1 to 4), middle range (moving average lines 5 to 8) and end range (moving average lines 9 to 12). These ranges helps to identify potential trend and market turn over. The moving average touch price is a relationship between the low price (uptrend) or high price (downtrend) with the moving average lines, it identifies where the price (low/high) has reached the the moving average line. Fetching the value to help for opening position, set stop loss and take profit. Since the stop loss is based and set from the previous moving average touch price value, when position is about to be open and setting the stop loss value, there is a verification to check both current and previous moving average touch price to recalculate the stop loss value. The turnover trend checks for a possible market turnover event, setting up a new profit target, this setting when enabled is to be helpful when a turnover occurs against the position to exit position with some profit based on highest high price if long or lowest low price if short. The turnover signal is similar to turnover trend. The difference is that when this setting is enabled and it triggers, it simply exit the current position and opens up a reverse position, long goes short and short goes long. And there is an complement optional that checks current price exit profitable. Entry Position Long Position: Price is higher than the mean moving averages. Meaning possible uptrend. The lines of the middle range from the moving averages are in increasing order. Meaning possible uptrend. The current high pierced up previous high. Fetch the previous value of the moving average touch price. Meaning the low price has touched one of the moving average lines, which that value is conditioning to open position. Short Position: Price is lower than the mean moving averages. Meaning possible downtrend. The lines of the middle range from the moving averages are in decreasing order. Meaning possible downtrend. The current low pierced down previous low. Fetch the previous value of the moving average touch price. Meaning the high price has touched one of the moving average lines, which that value is conditioning to open position. Risk Management Stop Loss: The stop loss is based from the previous moving average touch price value, high price for short and low price for long or occurs an verification to check for both current and previous moving average touch price value and a recalculation is done to set the stop loss. Take Profit: According to the author, the profit target should be at least 1:1.6 the risk, so to have the strategy mathematically positive. The profit target is configured input, can be increased or decreased. It calculates the take profit based on the price of the stop loss with the profit target input. Turnover Trend Long Position: The moving averages initial range lines signals a possible market turnover. Meaning long might be going short. Fetches the highest high hit since the opening of the position, setting that value to the new profit target. Short Position: The moving averages initial range lines signals a possible market turnover. Meaning short might be going long. Fetches the lowest low hit since the opening of the position, setting that value to the new profit target.
אסטרטגיית Pine Script®
מאת ‎AugustoErni‎
Economic Calendar EventsThis indicator provides an overlay of Events on the main chart, where each Event is visually represented by a Label and vertical Line, placed at the specified time interval for each Event. Events are defined by user data as an input string on the settings widget panel for the indicator. The event data is a string (semicolon delimited) whose grammar is a representation of a collection of Event records, where each Event record is a comma-separated list of fields, which correspond to: The name of the event. The symbol or ticker to which the Event applies (or `*` if it should apply to all ticklers). The timezone and then the year, month, day, hour, and minute of the event, respectively. Each Event record is separated by the semicolon ";" character. As an example , assume `evantData` is the string: "SVB,*,UTC,2023,03,10,00,00;US CPI,*,UTC,2023,04,12,08,30;ETH Shanghai,ETHUSD,UTC,2023,04,12,08,30" In the above case, there are 4 Events defined, three of which apply to all tickers and one applies only to ETHUSD, as follows: The first event is named SVB and applies to all tickers at UTC time on March 10, 2023 at 12:00:00. The second event is named US CPI and applies to all tickers at UTC time on April 12, 2023 at 08:30:00. The third event is named ETH Shanghai and applies to the ETHUSD ticker at UTC time on April 12, 2023 at 08:30:00. The fourth event is named FOMC Rates and applies to all tickers at UTC time on May 3, 2023 at 14:00:00. The following is a BNF for defining event data: market-events ::= event-record | event-record ";" market-events event-record ::= event-name "," ticker ”,” event-timezone "," event-time event-name ::= string event-time>::= year "," month "," day "," hour "," minute event-timezone ::= string ticker ::= "*" | string string ::= + year ::= {4} month ::= {2} day ::= {2} hour ::= {2} minute ::= {2}
אינדיקטור Pine Script®
מאת ‎foosmoo‎
5
[BTX] Triple TRIX + MAsThis indicator suggest a strategy, which is quite similar to multiple MA or multiple RSI strategies. This indicator can be used for all timeframes, all markets. This indicator can help detect the market trend and momentum. Default values are TRIX - 6, 12, and 24 periods and MA(8) for each TRIX line. You can choose what type of MA to be used (EMA or SMA). How to exploit this indicator? - When all of the lower TRIXs are ABOVE the higher one: TRIX(6) is above TRIX(12), and TRIX(12) is above TRIX(24), there is a BULLISH market. - When all of the lower TRIXs are BELOW the higher one: TRIX(6) is below TRIX(12), and TRIX(12) is below TRIX(24), there is a BEARISH market. - A crossover of the lower TRIX to the higher one indicates a BUY signal. - A crossunder of the lower TRIX to the higher one indicates a SELL signal. - TRIX crossover the Zero line can be considered as a STRONG bullish signal. - TRIX crossunder the Zero line can be considered as a STRONG bearish signal. - The MA of TRIX acts as a confirmation, it can be used as SELL signals. - High slopes of TRIX lines can point out the high momentum of the current trend. - Divergence patterns can be used with this indicator. - And many more tricks.
אינדיקטור Pine Script®
מאת ‎Batrachotoxin‎
2
Fib,Guppy Multiple MA(FGMMA)(A/D & Volume Weight,SMA,EMA)[cI8DH]Features: - 3 + 12 MAs (12 is chosen because Guppy has 12 MAs) - MA types can be set to Simple, Exponential, Weighted, and Smoothed - Volume weight can be applied to all available MAs (the built-in VWMA uses Simple MA) - It is possible to count in only effective portions of the volume in the equation by using Accum/Dist Volume Weight - Secondary smoothing (useful when volume weight is enabled) - Predefined MA sets based on Fibonacci sequence (2,3,5,8,.., 377), Guppy (3,5,8,10,12,15 &30,35,40,45,50,60), and cI8DH (2,3,5,8,12,17 & 30,34,39,45,52,60) Recommended settings: - hlc3 as input source captures all the essential information encapsulated in a candle. I'd use hlc3 as the default option. In uptrend, "low" and in downtrend, "high" might give more relevant results when using MAs for structural analysis of a market. For commonly used MAs (EMA20, SMA50,100,200), "close" should be used due to their self-fulfilling prophecy effect. - When you have volume weight above 0, you may want to use secondary smoothing. - Try not to use Simple MA for smaller lengths (below 20). Sharp changes in the past (right before the period specified by the length) will affect the current value of MA dramatically leading to confusion. - I am using the first 3 MAs for SMA 50,100,200. You can disable them from the MA type selector all at once when using Fib or Guppy ribbons. MA-based analysis: There are different ways of structuring a market. Geometrical (trend lines, channels, fans, patterns, etc) and Fib retracement-based structuring is very common among traders. MAs give an alternative way of analyzing markets. MA ribbons such as Guppy (6 slow and 6 fast-moving MAs) are popular for analyzing market flow. IMO default Guppy sets are a bit random as the numbers do not have an elegant sequence. So I proposed my sets based on increasing sequene spacing (+1). These two MA ribbons are good for market flow analysis but the spacing of the MAs are not ideal for structuring a market. Ribbons based on the Fib sequence is a better choice for structuring a market. This is the equivalent of Fib channels but in a more dynamic form. Among other things, MA Fib ribbon can be used to assess market momentum and to compare different stages of a market. Here are two "educational-only" examples: Notes: - Smoothed MA with length L = Exponential MA with length 2*L-1 - Read the background section in my ADP indicator to understand how A/D Volume is calculated
אינדיקטור Pine Script®
מאת ‎cI8DH‎
11
XPloRR MA-Trailing-Stop StrategyXPloRR MA-Trailing-Stop Strategy Long term MA-Trailing-Stop strategy with Adjustable Signal Strength to beat Buy&Hold strategy None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy. Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade. My buy strategy is triggered by the fast buy EMA (blue) crossing over the slow buy SMA curve (orange) and the fast buy EMA has a certain up strength. My sell strategy is triggered by either one of these conditions: the EMA(6) of the close value is crossing under the trailing stop value (green) or the fast sell EMA (navy) is crossing under the slow sell SMA curve (red) and the fast sell EMA has a certain down strength. The trailing stop value (green) is set to a multiple of the ATR(15) value. ATR(15) is the SMA(15) value of the difference between the high and low values. The scripts shows a lot of graphical information: The close value is shown in light-green. When the close value is lower then the buy value, the close value is shown in light-red. This way it is possible to evaluate the virtual losses during the trade. the trailing stop value is shown in dark-green. When the sell value is lower then the buy value, the last color of the trade will be red (best viewed when zoomed)(in the example, there are 2 trades that end in gain and 2 in loss (red line at end)) the EMA and SMA values for both buy and sell signals are shown as a line the buy and sell(close) signals are labeled in blue How to use this strategy? Every stock has it's own "DNA", so first thing to do is tune the right parameters to get the best strategy values voor EMA , SMA, Strength for both buy and sell and the Trailing Stop (#ATR). Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters) Then keep using these parameters for future buy/sell signals only for that particular stock. Do the same for other stocks. Important : optimizing these parameters is no guarantee for future winning trades! Here are the parameters: Fast EMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 10-20) Slow SMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 30-100) Minimum Buy Strength: minimum upward trend value of the Fast SMA Buy value (directional coefficient)(use values between 0-120) Fast EMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 10-20) Slow SMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 30-100) Minimum Sell Strength: minimum downward trend value of the Fast SMA Sell value (directional coefficient)(use values between 0-120) Trailing Stop (#ATR): the trailing stop value as a multiple of the ATR(15) value (use values between 2-20) Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now) compared to the Buy&Hold Strategy(=do nothing): BEKB(Bekaert): EMA-Buy=12, SMA-Buy=44, Strength-Buy=65, EMA-Sell=12, SMA-Sell=55, Strength-Sell=120, Stop#ATR=20 NetProfit: 996%, #Trades: 6, %Profitable: 83%, Buy&HoldProfit: 78% BAR(Barco): EMA-Buy=16, SMA-Buy=80, Strength-Buy=44, EMA-Sell=12, SMA-Sell=45, Strength-Sell=82, Stop#ATR=9 NetProfit: 385%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 55% AAPL(Apple): EMA-Buy=12, SMA-Buy=45, Strength-Buy=40, EMA-Sell=19, SMA-Sell=45, Strength-Sell=106, Stop#ATR=8 NetProfit: 6900%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 2938% TNET(Telenet): EMA-Buy=12, SMA-Buy=45, Strength-Buy=27, EMA-Sell=19, SMA-Sell=45, Strength-Sell=70, Stop#ATR=14 NetProfit: 129%, #Trade
אסטרטגיית Pine Script®
מאת ‎XPl0RR‎
8
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 :)
אינדיקטור Pine Script®
מאת ‎deimosaffair‎
9
BTC(Sats Stacking) - CDC Action zone filterType: Indicator (Pine v6) • Category: Strategy Tools / DCA • Overlay: Yes Overview This indicator simulates fixed-amount Bitcoin DCA (dollar-cost averaging) and lets you apply a CDC Action Zone filter to only buy in specific market conditions. It plots EMA(12/26) lines with a shaded zone (green when fast > slow, red when slow > fast), shows buy markers on the chart when a DCA event actually executes, and displays a concise performance table. The simulation tracks real invested capital (sum of your buys), not hypothetical equity injections, and reports PnL vs invested capital. Key features DCA frequency: Everyday, Every week, or Every month CDC filter: Buy on all days, only when CDC is Green (trend-up above fast EMA), or only when Red (trend-down below fast EMA) Execution price: Choose to buy at bar close or next bar open Capital controls: Fixed DCA amount per event, optional max budget cap Currency support: Portfolio currency label plus optional FX conversion (by symbol or manual rate) Chart visuals: Buy markers on candles; EMA(12/26) lines with shaded “action zone” Metrics table: Invested capital, buys executed, BTC accumulated, average price per BTC (quote), equity (portfolio), PnL% vs invested, and CAGR How it works CDC state: Green = EMA(fast) > EMA(slow) and price ≥ EMA(fast) Red = EMA(fast) < EMA(slow) and price < EMA(fast) DCA trigger: Fires on new day/week/month boundaries (timeframe-agnostic). Buy execution: When a DCA event occurs and passes the CDC filter and budget check, the script spends the fixed amount and adds the corresponding BTC at the chosen execution price. Inputs (highlights) Simulation Symbol (blank = current chart), Buy at close/open, DCA amount, Max total invested DCA Schedule Everyday / Every week / Every month CDC Action Zone Filter mode (All / Green only / Red only), Price source, Fast/Slow EMA lengths (defaults 12/26) Currency / Conversion Portfolio currency label, Convert on/off, By symbol (e.g., OANDA:USDTHB) or Manual rate Backtest Range Optional start/end dates Style Show EMA lines and zone, colors and opacities, buy marker size and color Display Show qty/price labels on buys, show metrics table, number formatting Metrics Invested capital: Sum of all DCA spends in your portfolio currency Equity (portfolio): BTC holdings marked to market and converted back if FX is enabled PnL % vs invested: (Equity / Invested - 1) × 100 CAGR: Based on elapsed time from first in-range bar to the latest bar Average price per BTC (quote): Spend in quote currency divided by BTC accumulated Notes This is an indicator, not a broker-connected strategy. It simulates buys and displays results without placing orders. For more realistic fills, use Buy at next bar open. If your portfolio currency differs from the symbol’s quote currency, enable Convert and supply a conversion symbol or manual rate. EMA shading is purely visual; the filter logic uses the same EMA definitions. Attribution & License Inspired by the DCA idea and community simulations; CDC filtering implemented with standard EMA(12/26) logic. License: MPL-2.0 (see code header). Author: MiSuNoJo Disclaimer This tool is for research and education only and is not financial advice. Past performance does not guarantee future results. Use at your own risk.
אינדיקטור Pine Script®
מאת ‎MiSuNoJo‎
Six Meridian Divine Swords [theUltimator5]The Six Meridian Divine Sword is a legendary martial arts technique in the classic wuxia novel “Demi-Gods and Semi-Devils” (天龙八部) by Jin Yong (金庸). The technique uses powerful internal energy (qi) to shoot invisible sword-like energy beams from the six meridians of the hand. Each of the six fingers/meridians corresponds to a “sword,” giving six different sword energies. The Six Meridian Divine Swords indicator is a compact “signal dashboard” that fuses six classic indicators (fingers)—MACD, KDJ, RSI, LWR (Williams %R), BBI, and MTM—into one pane. Each row is a traffic-light dot (green/bullish, red/bearish, gray/neutral). When all six align, the script draws a confirmation line (“All Bullish” or “All Bearish”). It’s designed for quick consensus reads across trend, momentum, and overbought/oversold conditions. How to Read the Dashboard The pane has 6 horizontal rows (explained in depth later): MACD KDJ RSI LWR (Larry Williams %R) BBI (Bull & Bear Index) MTM (Momentum) Each tick in the row is a dot, with sentiment identified by a color. Green = bullish condition met Red = bearish condition met Gray = inside a neutral band (filtering chop), shown when Use Neutral (Gray) Colors is ON There are two lines that track the dots on the top or bottom of the pane. All Bullish Signal Line: appears only if all 6 are strongly bullish (default color = white) All Bearish Signal Line: appears only if all 6 are strongly bearish (default color = fuchsia) The Six Meridians (Indicators) — What They Mean: 1) MACD — Trend & Momentum What it is: A trend-following momentum indicator based on the relationship between two moving averages (typically 12-EMA and 26-EMA) Logic used: Classic MACD line (EMA12−EMA26) vs its 9-EMA signal. Bullish: MACD > Signal and |MACD−Signal| > Neutral Threshold Bearish: MACD < Signal and |diff| > threshold Neutral: |diff| ≤ threshold Why: Small crosses can whipsaw. The neutral band ignores tiny separations to reduce noise. Inputs: Fast/Slow/Signal lengths, Neutral Threshold. 2) KDJ — Stochastic with J-line boost What it is: A variation of the stochastic oscillator popular in Chinese trading systems Logic used: K = SMA(Stochastic, smooth), D = SMA(K, smooth), J = 3K − 2D. Bullish: K > D and |K−D| > 2 Bearish: K < D and |K−D| > 2 Neutral: |K−D| ≤ 2 Why: K–D separation filters tiny wiggles; J offers an “extreme” early-warning context in the value label. Inputs: Length, Smoothing. 3) RSI — Momentum balance (0–100) What it is: A momentum oscillator measuring speed and magnitude of price changes (0–100) Logic used: RSI(N). Bullish: RSI > 50 + Neutral Zone Bearish: RSI < 50 − Neutral Zone Neutral: Between those bands Why: Centerline/adaptive bands (around 50) give a directional bias without relying on fixed 70/30. Inputs: Length, Neutral Zone (± around 50). 4) LWR (Williams %R) — Overbought/Oversold What it is: An oscillator similar to stochastic, measuring how close the close is to the high-low range over N periods Logic used: %R over N bars (0 to −100). Bullish: %R > −50 + Neutral Zone Bearish: %R < −50 − Neutral Zone Neutral: Between those bands Why: Uses a centered band around −50 instead of only −20/−80, making it act like a directional filter. Inputs: Length, Neutral Zone (± around −50). 5) BBI (Bull & Bear Index) — Smoothed trend bias What it is: A composite moving average, essentially the average of several different moving averages (often 3, 6, 12, 24 periods) Logic used: Average of 4 SMAs (3/6/12/24 by default): BBI = (MA3 + MA6 + MA12 + MA24) / 4 Bullish: Close > BBI and |Close−BBI| > 0.2% of BBI Bearish: Close < BBI and |diff| > threshold Neutral: |diff| ≤ threshold Why: Multiple MAs blended together reduce single-MA whipsaw. A dynamic 0.2% band ignores tiny drift. Inputs: 4 lengths (default 3/6/12/24). Threshold is auto-scaled at 0.2% of BBI. 6) MTM (Momentum) — Rate of change in price What it is: A simple measure of rate of change Logic used: MTM = Close − Close Bullish: MTM > 0.5% of Close Bearish: MTM < −0.5% of Close Neutral: |MTM| ≤ threshold Why: A percent-based gate adapts across prices (e.g., $5 vs $500) and mutes insignificant moves. Inputs: Length. Threshold auto-scaled to 0.5% of current Close. Display & Inputs You Can Tweak 🎨 Use Neutral (Gray) Colors ON (default): 3-color mode with clear “no-trade”/“weak” states. OFF: classic binary (green/red) without neutral filtering.
אינדיקטור Pine Script®
מאת ‎TheUltimator5‎
15
The Daily Bias Dashboard📜 Overview This indicator is a powerful statistical tool designed to provide traders with a probable Daily Bias based on historical price action. It is built upon the concepts of Quarterly Theory, which divides the 24-hour trading day into 4 distinct sessions to analyze market behavior. This tool analyzes how the market has behaved in the past to give you a statistical edge. It answers the question: "Based on the last X number of days, what is the most likely way the price will move during the Newyork AM & PM Sessions based on Asian & London Sessions?" ⚙️ How It Works The indicator divides the 24-hour day (based on the America/New_York timezone) into two 12-hour halves: First Half - 12 Hour Candle: The Accumulation/Manipulation or Asian/London Sessions (6 PM to 6 AM NY Time) This period covers the Asian session and the start of the London session. The indicator's only job here is to identify the highest high and lowest low of this 12-hour block, establishing the initial daily range. Second Half - 12 Hour Candle: The Distribution/Continuation or NY AM/PM Sessions (6 AM to 6 PM NY Time) This period covers the main London session and the full New York session. The indicator actively watches to see if, and in what order, the price breaks out of the range established in Session 1 (FIrst Half of the day). By tracking this behavior over hundreds of days, the indicator compiles statistics on four possible daily scenarios. 📊 The Four Scenarios & The Dashboard The indicator presents its findings in a clean, easy-to-read dashboard, calculating the historical probability of each of the following scenarios: ↓ Low, then ↑ High: The price first breaks the low of Session 1 (often a liquidity sweep or stop hunt) before reversing to break the high of Session 1. This suggests a "sweep and reverse" bullish day. ↑ High, then ↓ Low: The price first breaks the high of Session 1 before reversing to break the low of Session 1. This suggests a "sweep and reverse" bearish day. One-Sided Breakout: The price breaks only one of the boundaries (either the high or the low) and continues in that direction without taking the other side. This indicates a strong, trending day. No Breakout (Inside Bar): The price fails to break either the high or the low of Session 1, remaining contained within its range. This indicates a day of consolidation and low volatility. 🧠 How to Use This Indicator This is a confluence tool, not a standalone trading system. Its purpose is to help you frame a high-probability narrative for the trading day. Establish a Bias: Start checking the dashboard at 06:00 AM Newyork time, which is the start of next half day trading session. If one scenario has a significantly higher probability (e.g., "One-Sided Breakout" at 89%), you have a statistically-backed directional bias in the direction of Breakout. 🔧 Features & Settings Historical Days to Analyze: Set how many past days the indicator should use for its statistical analysis (default is 500). Session Timezone : The calculation is locked to America/New_York as it is central to the Quarterly Theory concept, but this setting ensures correct alignment. Dashboard Display: Fully customize the on-screen table, including its position and text size, or hide it completely. ⚠️ Important Notes For maximum accuracy, use this indicator on hourly (H1) or lower timeframes. The statistical probabilities are based on past performance and are not a guarantee of future results. This tool is designed to sharpen your analytical skills and provide a robust, data-driven framework for your daily trading decisions. Use it to build confidence in your directional bias and to better understand the rhythm of the market. Disclaimer: This indicator is for educational and informational purposes only and does not constitute financial advice. All trading involves risk.
אינדיקטור Pine Script®
מאת ‎traderprimez‎
3
NQ Phantom Scalper Pro# 👻 NQ Phantom Scalper Pro **Advanced VWAP Mean Reversion Strategy with Volume Confirmation** ## 🎯 Strategy Overview The NQ Phantom Scalper Pro is a sophisticated mean reversion strategy designed specifically for Nasdaq 100 (NQ) futures scalping. This strategy combines Volume Weighted Average Price (VWAP) bands with intelligent volume spike detection to identify high-probability reversal opportunities during optimal market hours. ## 🔧 Key Features ### VWAP Band System - **Dynamic VWAP Bands**: Automatically adjusting standard deviation bands based on intraday volatility - **Multiple Band Levels**: Configurable Band #1 (entry trigger) and Band #2 (profit target reference) - **Flexible Anchoring**: Choose from Session, Week, Month, Quarter, or Year-based VWAP calculations ### Volume Intelligence - **Volume Spike Detection**: Only triggers entries when volume exceeds SMA by configurable multiplier - **Relative Volume Display**: Real-time volume strength indicator in info panel - **Optional Volume Filter**: Can be disabled for testing alternative setups ### Advanced Time Management - **12-Hour Format**: User-friendly time inputs (9 AM - 4 PM default) - **Lunch Filter**: Automatically avoids low-liquidity lunch period (12-2 PM) - **Visual Time Zones**: Color-coded background for active/inactive periods - **Market Hours Focus**: Optimized for peak NQ trading sessions ### Smart Risk Management - **ATR-Based Stops**: Volatility-adjusted stop losses using Average True Range - **Dual Exit Strategy**: VWAP mean reversion + fixed profit targets - **Adjustable Risk-Reward**: Configurable target ratio to opposite VWAP band - **Position Sizing**: Percentage-based equity allocation ### Optional Trend Filter - **EMA Trend Alignment**: Optional trend filter to avoid counter-trend trades - **Configurable Period**: Adjustable EMA length for trend determination - **Toggle Functionality**: Enable/disable based on market conditions ## 📊 How It Works ### Entry Logic **Long Entries**: Triggered when price touches lower VWAP band + volume spike during active hours **Short Entries**: Triggered when price touches upper VWAP band + volume spike during active hours ### Exit Strategy 1. **VWAP Mean Reversion**: Early exit when price returns to VWAP center line 2. **Profit Target**: Fixed target based on percentage to opposite VWAP band 3. **Stop Loss**: ATR-based protective stop ### Visual Elements - **VWAP Center Line**: Blue line showing volume-weighted fair value - **Green Bands**: Entry trigger levels (Band #1) - **Red Bands**: Extended levels for target reference (Band #2) - **Orange EMA**: Trend filter line (when enabled) - **Background Colors**: Yellow (lunch), Gray (after hours), Clear (active trading) - **Info Panel**: Real-time metrics display ## ⚙️ Recommended Settings ### Timeframes - **Primary**: 1-5 minute charts for scalping - **Validation**: Test on 15-minute for swing applications ### Market Conditions - **Best Performance**: Ranging/choppy markets with good volume - **Trend Markets**: Enable trend filter to avoid counter-trend trades - **High Volatility**: Increase ATR multiplier for stops ### Session Optimization - **Pre-Market**: Generally avoided (low volume) - **Morning Session**: 9:30 AM - 12:00 PM (high activity) - **Lunch Period**: 12:00 PM - 2:00 PM (filtered by default) - **Afternoon Session**: 2:00 PM - 4:00 PM (good volume) - **After Hours**: Generally avoided (wide spreads) ## ⚠️ Risk Disclaimer This strategy is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Trading futures involves substantial risk of loss and is not suitable for all investors. Users should: - Thoroughly backtest on historical data - Start with small position sizes - Understand the risks of leveraged trading - Consider transaction costs and slippage - Never risk more than you can afford to lose ## 📈 Performance Tips 1. **Volume Threshold**: Adjust volume multiplier based on average NQ volume patterns 2. **Band Sensitivity**: Modify band multipliers for different volatility regimes 3. **Time Filters**: Customize trading hours based on your timezone and preferences 4. **Trend Alignment**: Use trend filter during strong directional markets 5. **Risk Management**: Always maintain consistent position sizing and risk parameters **Version**: 6.0 Compatible **Asset**: Optimized for NASDAQ 100 Futures (NQ) **Style**: Mean Reversion Scalping **Frequency**: High-Frequency Trading Ready
אסטרטגיית Pine Script®
מאת ‎TradeManager_PRO‎