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Macro

Moving Average Macro Trend FilterA simple indicator that adds a background fill to your chart based on a fast and slow moving average. When the fast moving average is above the slow moving average, the background is green. When the slow moving average is above the fast moving average, the background is red. If the fast and slow moving average don't agree on direction, the background is yellow. Pick between EMA, SMA, RMA, WMA Select different timeframes for chart, slow MA, and fast MA
Pine Script™指标
由jordanfray提供
1
Macro EMA Correlation This script is useful to see correlation between macroeconomic assets, displayed in different ema line shown in percentage to compare these assets on the same basis. Percentage will depend on the time frame selection. In the higher timeframe you will see higher variation and in small timeframe smaller variation. You can select the timeframe who suit your trading style. The 1h and 4h fit well for longer trend swing trade and the lower time frame 15m, 5m, 1m are good for scalping or daily trading. The following asset are available: Bitcoin Ethereum Gold Crypto total market cap excluding bitcoin (total2) United state 10-year government bond (US10Y) Usdt dominance show the concentration of usdt hold. For example, when trader are fearful they sell their crypto position to keep more usdt in their portfolio (USDT.D) The USD/JPY pair the dollar usd versus the Japanese Yen one of the most forex traded pair. You can clic on parameter to select the asset you want to analyse. The main correlation observed are: bitcoin negatively correlated with the usdt dominance. bitcoin negatively correlated with the usd/jpy pair bitcoin is positively correlated to eth, total2 (altcoin) bitcoin positively correlated with gold bitcoin is mostly negatively correlated to us10y The basis of correlation is that positively correlated asset goes in the same direction and that the negatively correlated goes in opposite direction. So, the idea is to use these information to see trend reversing. Example 1: when bitcoin and usdt dominance are extended in opposite direction we look for a possible retracement toward 1% wich is the middle base. Example 2 : when bitcoin make a move we look for ethereum and total 2 to follow
Pine Script™指标
由F_rank_01提供
MicroStrategy MetricsA script showing all the key MSTR metrics. I will update the script every time degen Saylor sells some more office furniture to buy BTC. All based around valuing MSTR, aside from its BTC holdings. I.e. the true market cap = enterprise value - BTC holdings. Hence, you're left with the value of the software business + any premium/discount decided by investors. From this we can derive: - BTC Holdings % of enterprise value - Correlation to BTC (in this case we use CME futures...may change this) - Equivalent Share Price (true market cap divided by shares outstanding) - P/E Ratio (equivalent share price divided by quarterly EPS estimates x 4) - Price to FCF Ratio (true market cap divided by FCF (ttm)) - Price to Revenue (^ but with total revenue (ttm))
Pine Script™指标
由Pinnacle_Investor提供
5
Fear/Greed IndexMy goal was to create something akin to the Fear & Greed Index ( money.cnn.com ) that CNN and others do. A Fear/Greed Index can be used by any trader or investor but I believe it's best viewed with a contrarian's eye-- When the market appears to be signalling Extreme Fear, that is a good place to start buying from emotional players who want to sell no matter the price When signalling Extreme Greed, that may be a good place to start taking profits off or getting hedged, as there may be too much exuberance in the air Important to note and remember, however, is that there can often times be fear in the air for good reasons! I like to see this as if we dip into extreme fear and return shortly after, the fear may warrant constraint from buying, or returning back to extreme greed may be a very strong market extension The script draws from several other tickers which I have read and personally observed to be decent macro correlations for the stock market (specifically the SP500). For the state of each of these metrics I gave a rating, good or bad, then added them together and put it into your standard Stochastic. These macro correlations include-- The % of stocks in the SP500 above multiple Simple Moving Average lengths VIX and its term-structure (contango, backwardation) Treasury Bonds Gold Junk/High Yield Bonds The Put/Call Ratio The SP500 Options Skew Advancing and Declining Issues On some of these I opted to use a function for the Relative Momentum Index instead of RSI, as the RMI oscillates better (in my opinion). I also used a Band-Pass Filter/Double EMA for smoothing the results of the stochastic. A LOT of these numbers were made to my own observation and discretion and can get out-dated over time. With that said, PLEASE feel free to revise, fine tune, modify this as you wish to optimize yours. And please let me know if I have made any mistakes here or something should be added.
Pine Script™指标
由chrism665提供
11
Macroeconomic Artificial Neural Networks This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index. No technical analysis data were used. The average error rate is 0.01. In this respect, there is a strong relationship between the index and macroeconomic data. Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. ) Macroeconomic Parameters Effective Federal Funds Rate (FEDFUNDS) Initial Claims (ICSA) Civilian Unemployment Rate (UNRATE) 10 Year Treasury Constant Maturity Rate (DGS10) Gross Domestic Product , 1 Decimal (GDP) Trade Weighted US Dollar Index : Major Currencies (DTWEXM) Consumer Price Index For All Urban Consumers (CPIAUCSL) M1 Money Stock (M1) M2 Money Stock (M2) 2 - Year Treasury Constant Maturity Rate (DGS2) 30 Year Treasury Constant Maturity Rate (DGS30) Industrial Production Index (INDPRO) 5-Year Treasury Constant Maturity Rate (FRED : DGS5) Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES) Civilian Employment Population Ratio (EMRATIO) Capacity Utilization (TOTAL INDUSTRY) (TCU) Average (Mean) Duration Of Unemployment (UEMPMEAN) Manufacturing Employment Index (MAN_EMPL) Manufacturers' New Orders (NEWORDER) ISM Manufacturing Index (MAN : PMI) Artificial Neural Network (ANN) Training Details : Learning cycles: 16231 AutoSave cycles: 100 Grid Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 998 Validating example rows: 0 Querying example rows: 0 Excluded example rows: 0 Duplicated example rows: 0 Network Input nodes connected: 19 Hidden layer 1 nodes: 2 Hidden layer 2 nodes: 0 Hidden layer 3 nodes: 0 Output nodes: 1 Controls Learning rate: 0.1000 Momentum: 0.8000 (Optimized) Target error: 0.0100 Training error: 0.010000 NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market. Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market ) I hope it will be useful in your studies and analysis, regards.
Pine Script™指标
由Noldo提供
38