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DATABTC formed bullish Gartley | Upto 24% expectedIt Moved Up More Than I Was Expecting: I posted an idea on DATA with Bitcoin pair on 5 Oct 2019 here the price action was moving within a down channel and hitting the support of this channel the volume profile of complete channel was showing very low interest of traders at support and the price action found the support at 0.00000427 price action found the support at 0.00000098 within this channel the RSI was oversold and MACD turned strong bullish and Stochastic gave bull cross from oversold based on these indications I predicted that the price action will give upto 237% but when the price action took bullish divergence it was more powerful than I was expected and it produced 1025% very very huge gains below was the post: Another Bullish Pattern On Short Term Chart: Now this time the pricline of DATA has formed a bullish Gartley pattern on very small 2 hour time period based chart and this would be really good opportunity for DATABTC day or short term traders. The pattern is driven as below: After initial leg (X to A) the A to B leg is retraced between 0.618 to 0.786 Fibonacci and then B to C leg is projected between 0.382 to 0.886 of A to B leg's Fibonacci projection and final (C to D) leg is retraced between 0.618 to 0.786 Fibonacci as perfectly required for Gartley pattern and now the price action is moving in potential reversal zone of this Gartley pattern, now we can expect a bullish divergence from this level very soon. The Buying And Sell Targets: As per this harmonic Gartley pattern the buying and sell targets should be: Buy between: 0.00000193 to 0.00000181 sats Sell between: 0.00000203 to 0.00000226 sats So this very short term trade has potential to produce upto 24% gains. What Should Be Our Stop Loss: The potential reversal zone area can be used as stop loss in case of complete candle stick closes below this level. I will be keep posting more potential trading ideas for educational purpose on different assets as soon as will receive any trading signals.
Long
by moon333
DATABTC hitting the support | Upro 237% possibilityPriceline of DATAcoin / Bitcoin cryptocurrency is moving within a channel. Previously in month of Mar 2019 the price action found the support at 0.00000427 sats and took bullish divergence and produced more than 93% profit. This time again the price action has found another support and current support is at 0.00000098 sats and we are expecting next bullish divergence form here like it happened Mar 2019, insha Allah This 0.00000098 sats support is our stop loss in case of complete candle stick closes below this support. RSI was oversold and now moving up. Stochastic has given bull cross from oversold zone. Volume profile of complete channel is showing less interest of traders below the support. I have defined targets using Fibonacci sequence: Sell between: 0.00000211 to 0.00000331 Regards, Atif Akbar (moon333)
Long
by moon333
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
Backtesting Became Cool Again!Hello traders Hope you're all doing fantastic I learned a few weeks ago that TradingView released a CSV Export feature. Basically, you can export any indicator outputs/plots and get the data in your favorite Excel/Google Sheet/Open office, etc. Using that software is relatively easy and learning how to construct pivot tables/charts will expand your analytics beyond the realm of what you thought was even possible... #way #too bold #statement In that video: I exported the data provided by Backtest Premium Suite in Google Sheet In Google Sheet, I built a pivot table and a few pivot charts (requires a few clicks only) Allows me to get insightful analytics and understand better where I can improve (how much opportunity do I capture? for which risk? are my winners increasing faster than my losers are decreasing?...) Thank you TradingView for enabling this feature. All the BEST Dave
BITMEX:BTCUSD.PEducation
by Daveatt
4