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Array

Array Technologies receives buy rating from CitibankArray Technologies, Inc., a leading manufacturer of solar trackers, recently received positive remarks from Citibank, boosting investor confidence. Despite facing current operational challenges, including the firing of its CFO, increased transportation costs, project delays, and inflation pressures impacting margins, Citibank has upgraded Array Technologies' stock from neutral to "buy." Analysts underscore the company's long-term growth potential, particularly highlighting its innovative smart system launched in spring 2024, which leverages weather forecasts to protect solar panels from hail damage. Technical analysis of Array Technologies, Inc. (NASDAQ: ARRY) Let’s review the potential trading opportunities based on the technical aspects of Array Technologies' stock: Timeframe : Daily (D1) Current trend : An uptrend is beginning to form, indicating potential for significant growth. Resistance level : 12.05 USD Support level : 9.40 USD Potential downtrend target : Should the stock enter a downtrend, the downside target could be around 6.50 USD Short-term target : If the uptrend continues and the stock breaches the resistance at 12.05 USD, a short-term target could be set at 16.30 USD Medium-term target : With sustained positive momentum, the stock price might aim for 20.15 USD Investors should keep a close eye on Array Technologies, particularly as the market responds to Citibank's positive assessment. The company's strategic initiatives to navigate current challenges and its innovations in solar technology suggest a positive outlook. — Ideas and other content presented on this page should not be considered as guidance for trading or an investment advice. RoboMarkets bears no responsibility for trading results based on trading opinions described in these analytical reviews. The material presented and the information contained herein is for information purposes only and in no way should be considered as the provision of investment advice for the purposes of Investment Firms Law L. 87(I)/2017 of the Republic of Cyprus or any other form of personal advice or recommendation, which relates to certain types of transactions with certain types of financial instruments. Risk Warning: CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 65.68% of retail investor accounts lose money when trading CFDs with this provider. You should consider whether you understand how CFDs work and whether you can afford to take the high risk of losing your money.
NASDAQ:ARRY
by RoboMarkets
1
Development Log for Neural Network PrototypeThe idea, at the core: Port a limited RNN/LSTM Neural Network model from Python with a reduced training set and dimension size for layers to demonstrate that a fully functional (even if limited) Neural Net can work in Pine. Limited model + having the python code on hand = Able to test and verify components in Pine at every step, in theory The model/script I'm attempting to implement a limited subset of is detailed here: iamtrask.github.io A dataset in binary is required, but binary does not exist in pinescript, thus: To do this, decimal to binary and binary to decimal functions are required. This didn't exist previously - I've written a script to accomplish just that: Originally, this was going to have a input_dim of 2, hidden_dim of 16, but I've changed the hidden_dim to 8 (binary dimensions from 8 to 5) to reduce the dataset range to max 32 while I figure out to implement working pseudo-arrays and state updates. I've looked at RicardoSantos's scripts for Markov and Pseudoarrays, and will be using them as a reference going forward. I've verified the output of the Sigmoid function and 1st derivative of the Sigmoid function in Python for values of (-1,0,0.5,1 ). I've yet to publish the Sigmoid script pending approval from TV moderators about including python code that is commented out at the bottom to verify the results of that script. What I'm trying to do here with training dataset generation was unsuccessful, for multiple reasons: Lack of formal array constructs in pine Psuedorandom Number generator limitations Manual state weighting and updating as per RicardoSantos's Function Markov Process is required: What's being plotted for are the first three layers, but without the full range of the input_dimensions, hidden_dimensions: syn_0 (blue) syn_1 (green) syn_h (red) While there's more than a few technical hurdles to overcome (i.e. potential pine issues from max variables to runtime/compile limits, no real arrays, functions to do state updates RichardoSantos Markov Function style, etc), I'm fairly confident a limited working model should be possible to create in Pine.
COINBASE:BTCUSDEducation
by auroagwei
4