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#PYTH/USDT#PYTH The price is moving in a bearish channel pattern on the 4-hour frame, and it adheres to it well, and it is expected to break to the upside The price rebounded well from the lower border of the channel at the green support level of 0.4220, which is a strong level We have a trend to hold above the Moving Average 100, which is strong support for the rise We have very strong oversold saturation on the RSI indicator to support the rise, with a downtrend about to break higher Entry price is 0.4380 The first target is 0.5146 The second target is 0.5747 The third target is 0.6550
Long
by CryptoAnalystSignal
Market Sentiment and Trend Prediction System. Predictive Model. The codes listed below (free&easy;), detailed steps to follow for developing the event prediction system: 1. **Collecting Data**: we will need to gather data from various sources. We can use Python-based web scraping libraries like Beautiful Soup and Scrapy to extract data from news websites and social media platforms (scraping exports data from websites, it is safe and legal, but better contact website admins and ask for authorization) 2. **Cleaning and Preprocessing Data**: After collecting the data, we need to clean and preprocess it. We can use Python libraries like Pandas and NumPy to remove duplicates, missing values, and irrelevant information. 3. **Natural Language Processing**: Once the data is cleaned, we can use natural language processing (NLP) techniques to extract insights from the text data. For example, we can use the NLTK library to perform tokenization, stemming, and lemmatization on the text data. 4. **Model Building**: We can use machine learning algorithms like Random Forest, Gradient Boosted Trees, or Support Vector Machines (SVMs) to build predictive models. These models can help us predict the occurrence of an event or the sentiment associated with a specific topic. 5. **Dashboard and Visualization**: Finally, we can create an intuitive dashboard using tools like Tableau or Power BI to display the analyzed data in real-time. We can use interactive visualizations like bar graphs, pie charts, and heat maps to provide users with a clear understanding of the events and their impacts. 6. **Testing and Deployment**: Once the system is developed, we need to test it thoroughly to ensure that it is delivering the expected results. We can use various testing frameworks like pytest, unittest, or nosetests to automate the testing process. Once testing is completed, we can deploy the system to the production environment. 7. **Regular Maintenance and Updates**: We also need to ensure that the system is continuously monitored. The codes : Termux (the app is in playstore, github etc, to excute python files, or commands,for every step, some general commands and libraries that you might find useful: 1. Collecting Data: - To install Scrapy, run `pip install scrapy`. - To install Beautiful Soup, run `pip install beautifulsoup4`. - To scrape data from a webpage using Scrapy, run `scrapy crawl `. - To scrape data from a webpage using Beautiful Soup, use Python's built-in `urllib` or `requests` module to fetch the webpage's HTML. Then, use Beautiful Soup to parse the HTML and extract the relevant data. 2. Cleaning and Preprocessing Data: - To install Pandas, run `pip install pandas`. - To install NumPy, run `pip install numpy`. - To remove duplicates, use Pandas' `drop_duplicates()` function. - To remove missing values, use Pandas' `dropna()` function. - To filter out irrelevant data, use Pandas' indexing functions like `loc` and `iloc`. 3. Natural Language Processing: - To install NLTK, run `pip install nltk`. - To perform tokenization, run `nltk.tokenize.word_tokenize(text)`. - To perform stemming, run `nltk.stem.PorterStemmer().stem(word)`. - To perform lemmatization, run `nltk.stem.WordNetLemmatizer().lemmatize(word)`. 4. Model Building: - To install scikit-learn, run `pip install scikit-learn`. - To instantiate a Random Forest classifier, run `from sklearn.ensemble import RandomForestClassifier; clf = RandomForestClassifier()`. - To fit the model to the data, run `clf.fit(X_train, y_train)`, where `X_train` is the input data and `y_train` is the output labels. - To use the model to make predictions, run `clf.predict(X_test)`. 5. Dashboard and Visualization: - To install Tableau, follow the instructions on their website. - To install Power BI, follow the instructions on their website. - To create a bar graph in Python, use the `matplotlib` library: `import matplotlib.pyplot as plt; plt.bar(x, y); plt.show()`. - To create a pie chart in Python, use `plt.pie(values, labels=labels); plt.show()`. - To create a heat map in Python, use `sns.heatmap(data, cmap='coolwarm'); plt.show()` (assuming you have installed the Seaborn library). These are general commands and libraries that you can use as a starting point. If you need me to explain how to use termux, let me know.
Education
by Moroccolover
3
Bitcoin Price Weekly Analysis BINANCE:BTCUSDT weekly analysis, 1. we are in a very important price range, the weekly price for a forth time entered our very important range (24311 - 255848) range, If we can not path thorough the price and go above the 28850 range, we might see BTC still without a bullish power. 2. It's also clear to see that currently the bearish side is still controlling the most of the liquidity, bullish power is small but still trying hard. 3. the price respect to the 21306.82 level is perfect and something we want to see. IN general, we need to wait some more time to confirm the trend of BTC, as it is bullish ? bearish ? I recommend do your own analysis and do not trust whatever people talking on the web, people are manipulating and hoping to take your money. we know it...
by azataiot
Prices are programmed.BINANCE:BTCUSDT monthly analysis, we are in a very important range, first we entered a lower bullish FVG around 14k - 17k (check the very detailed and accurate pricing on your chart) then it rejected as expected. Now it entered another important region where it failed to broke up, but stayed below 24k-26k range, which means a lot. Currently it is very hard to tell which way BTC is heading, but it will be clearly seen after a month, as we are on that position, it can test the 24k-26k region as a resistance level and turn down for more bearish scenario or it acts the 14k-17k as a bullish confirmation and makes the upper 24k-26k regions as a new support and rally up, after then, one very important price level will be the 28.8k . Let's see.
by azataiot
Easy to get data from cnbc.com, it is freeIt is a note for developer Sometime, we need stock data to write strategy checking logic by python. Data from cnbc.com is good and it is free. How to get it? There are 2 steps to do: Step 1: install library python pip install cnbcfinance Step 2: Get history or get quote realtime # Get history data from cnbcfinance import get_history_df data = get_history_df('AAPL', '30m') # Get quote from cnbcfinance import get_history_df data = get_quota('AAPL')
NASDAQ:AAPLEducation
by binancash
1
Statistical approach to risk management - Python scriptThis script can be used to approximate a strategy, and find optimal leverage. The output will consist of two columns, one for the median account size at end of trading, and one for the share of accounts liquidated. The script assumes a 100% position size for the account. This does not take into account size deviations for earnings and losses, so use with a grain of salt if your positions vary greatly in that aspect. Code preview cdn.discordapp.com/attachments/592684708551327764/848701541766529034/carbon.png TradingView does not allow posting external links until you've reached a specific reputation, so i can't use the url feature Input explanation WINRATE : chance of winning trade AVGWIN : average earning per winning trade AVGLOSS : average loss per losing trade MAX_LEVERAGE : maximum leverage available to you TRADES : how many trades per account you want to simulate ACCOUNTS : how many accounts you want to simulate the inputs used in the source code are from one of my older strategies, change them to suit your algorithm Source code pastebin.com/69EKdVFC Good luck, Have fun -Vin
VEducation
by Vindruee
ELA/USDT Update: The recovery startet. Get ready for 8x.Elastos did do a retest eventhough it did not touch are drawn box. We are heading upwards now and rightly so. Checkout the roadmap of elastos here: elastos.info Sidechain proposals will be updated in May 2021. Java and Python are coming before Q4. The volume keeps up which is a good sign. Basic rules: - Never buy the top/ATH - Take profit as long as you can - Use Stop/loss for leveraged positions - If you are not experienced, don't leverage in the first place Enjoy the ride and don't be too greedy. If you like the content, please like, comment and give this channel a follow. Always do your own research and keep in mind that my charts and comments cannot be considered financial advice. Cheers ps. Chart explanation: Main lines: - Green lines are tested support lines. - Orange lines are resistance lines or, if we are above, possible support lines which were not tested yet. - Cyan line is for volume trendline. Helplines: - Purple lines are trendlines we take a look at. - Yellow lines are for visual help only. Boxes: - Either entry zone or support zone. Check the description.
Long
by tumbler55
CTSI is Undervalued fundamentally - Huge supportThe biggest shortage in smart contract/defi crypto space is developers. CARTESI (CTSI) has been working for years and aims to be a layer 2 solution for "existing developers" to build out smart contract with high throughput (ETH ..cough) and low transaction fees. When I say existing developers, I mean all developers that make our other stuff and have done for decades in the real world before crypto. CARTESI is blockchain agnostic and is extremely welcoming as a workable platform for most of the developers who would not have a frickin clue what Solidity is. This opens up more development on this layer by default of sheer numbers, similar to why there are more Android developers vs IOS in the mobile phone arena. Now this is still a speculation, but if successful, this is a true game changer. Plus on the technicals, we have side stepped out of a bearish trend and I simply think this project should exist. If not this team, then someone will do it. I have watched price double, quadruple and settle back down. Now that the Degens are out or have at least settled down I still like what I see. There is a massive market of people actively looking at low cap tokens and I feel retail speculators will drive this token up because they fell in love with the vision (which totally is needed) and the fact anyone can understand what the utility is - IE use case. I am proudly one of them, so I want at least some exposure if this train pull away. **Please use proper risk management**
Long
by Silverbacked_Gorilla
3