Kunden der Postbank, die nicht schnell genug antworten, müssen künftig mit einer Bearbeitungsgebühr von 79,95 Euro rechnen. Das geht aus einem aktuellen Schreiben hervor. Doch was steckt wirklich hinter der neuen Gebühr? Der Beitrag Postbank: Drohen bald Strafzahlungen von 80 Euro? erschien zuerst auf inside digital.
Um E-Autos ranken sich viele Mythen, die schwer zu entkräften sind. Die vielleicht größte Legende versucht die Sachverständigenorganisation DEKRA nun zu widerlegen. Und das dürfte Wasser auf die Mühlen vieler E-Auto-Fans sein. Der Beitrag DEKRA entzaubert größten Mythos um E-Autos erschien zuerst auf inside digital.
Stardew Valley Expanded lässt euch das Kultspiel wie neu erleben. Es gibt kaum eine bessere Art, um wieder in Stardew Valley einzusteigen – und hunderte Stunden darin zu versenken.
Habt ihr Urlaub? Braucht ihr 28 kostenlose E-Books? Dann seid ihr hier richtig! Ich habe jede Menge Weihnachtsschnulzen für euch bei Amazon gefunden. Außerdem aber auch spannende Krimis und überraschende Fantasy-Storys. Greift zu und nutzt die Zeit zum Lesen.
? CRYPTOCAP:XRP in Perspective: How Will the SEC Lawsuit Impact Price? ? The ongoing legal battle between Ripple (XRP) and the U.S. Securities and Exchange Commission (SEC) has become a defining moment in the crypto world. With the case still unfolding, many investors are left questioning: How will the lawsuit impact XRP's price? --- ⚖️ The SEC Lawsuit: A Quick Overview In December 2020, the SEC filed a lawsuit against Ripple Labs, alleging that XRP is an unregistered security and that its sale violated U.S. securities laws. This legal uncertainty has weighed heavily on XRP’s price and its investor sentiment, with the outcome potentially shaping the future of the cryptocurrency. --- ? Potential Scenarios for XRP’s Price ✅ Ripple Wins the Case ? Positive Price Impact: A Ripple victory or favorable settlement could eliminate the legal uncertainty surrounding XRP, leading to a major price surge as confidence returns. ? Increased Adoption: Clarity on XRP's status as a non-security may attract more institutional and retail investors, boosting liquidity and demand. ? Market Rally: A favorable ruling could spark broader optimism in the altcoin market, benefiting other digital assets as well. ❌ Ripple Loses the Case ? Negative Price Impact: A loss might result in penalties or XRP being registered as a security, significantly affecting its liquidity and availability on U.S. exchanges. ? Market Panic: The ruling could trigger a sell-off as investors react to the unfavorable outcome, adding downward pressure to XRP’s price. ? Prolonged Legal Uncertainty ? Price Volatility: As the case drags on, speculation may dominate, causing erratic price movements. Investors might tread cautiously, creating an environment of uncertainty and fluctuation. --- ? What’s Next? For XRP investors, staying informed about the lawsuit's developments and broader market trends is critical. Whether you’re holding or planning to invest, being proactive and understanding the potential outcomes can help navigate this volatile situation. --- ? Your Take? How do you see the SEC lawsuit shaping XRP’s future? Share your thoughts below!
BTC is reviving after the much anticipated fall. The trendline is broken and there a chance that we see the upside from here. I am looking for an upside till the next distribution level reaches
I hope this chart provides clear simple levels to trade or fade
Wave A is the fastest and strongest wave on the chart, indicating that the long-term trend is up, and any downward wave is counter-trend. The pattern is a flat correction, confirmed complete in April 2024 with the drop from $52 to $18. Currently, we appear to be in an X wave. The X wave can be labeled as small or large based on its retracement of the entire pattern. Since it is not yet complete, I cannot provide further details based on the monthly chart. However, the price is likely to fully retrace the $13 low, and it should head lower.
Further down the path, it can form a diametric pattern.
// @ Julien_Eche //@version=5 indicator('Machine Learning Signal Filter', '', true) //**** Display Options **** displayMetrics = input(true, 'Display Neural Metrics?') dataCurves = input(false, 'Display Signal Curves?') //**** Signal Calculations **** signalEaster = input(true, 'Activate Signal Calculations?') useDataForSignal = input(true, 'Use Data for Signal Generation?') //**** Trade Settings **** lot_size = input.float(0.1, 'Signal Lot Size', options= ) stasisPeriod = input.int(5, 'Temporal Stasis Period (minimum 1)', minval=1) //**** Signal Filtration **** filterMatrix = input.string('Volatility', 'Signal Filtration Mode', options= ) //**** Temporal and Data Settings **** dataFlux = input.string('Close', 'Data Type', options= ) chronoSync = input.timeframe('', 'Temporal Dimension') retroScan = input.int(5, 'Chrono Window (minimum 2)', minval=2) chronoNorm = input.int(100, 'Temporal Normalization (range: 2 to 240)', minval=2, maxval=240) //**** Neural Network Parameters **** nanoPulse = input.float(0.001, 'Neural Pulse Rate (range: 0.0001 to 0.01)', minval=0.0001, maxval=0.01, step=0.0001) chronoCycles = input.int(1500, 'Cycle Iterations (range: 50 to 20000)', minval=50) //**** Table Position and Text Size Input **** tablePosition = input.string(defval = "Top Right", title = "Table Position", options = ) textSizeInput = input.string("Normal", "Text Size", options= ) //**** Function to get the table position **** getTablePosition(pos) => switch pos "Top Right" => position.top_right "Bottom Right" => position.bottom_right "Middle Right" => position.middle_right "Bottom Center" => position.bottom_center "Middle Left" => position.middle_left //**** Function to get text size **** getTextSize(size) => switch size "Normal" => size.normal "Large" => size.large "Small" => size.small //**** Constants and Variables **** var ACTIVATE = 1 var DEACTIVATE = -1 var STASIS = 0 var signalBeacon = STASIS var timeTracker = 0 var float start_long_trade = 0. var float long_trades = 0. var float start_short_trade = 0. var float short_trades = 0. var int victories = 0 var int trade_counter = 0 var table metricsTable = na //**** Helper Functions **** cosmicBlue(qz) => qz > 9 ? #FF0000ff : qz > 8 ? #FF0000e5 : qz > 7 ? #FF0000cc : qz > 6 ? #FF0000b2 : qz > 5 ? #FF000099 : qz > 4 ? #FF00007f : qz > 3 ? #FF000066 : qz > 2 ? #FF00004c : qz > 1 ? #FF000033 : #FF000019 nebulaRed(qz) => qz > 9 ? #0000FFff : qz > 8 ? #0000FFe5 : qz > 7 ? #0000FFcc : qz > 6 ? #0000FFb2 : qz > 5 ? #0000FF99 : qz > 4 ? #0000FF7f : qz > 3 ? #0000FF66 : qz > 2 ? #0000FF4c : qz > 1 ? #0000FF33 : #0000FF19 quantumSurge(threshold) => rsiVolume = ta.rsi(volume, 14) oscillator = ta.hma(rsiVolume, 10) oscillator > threshold fluxThreshold(minVol, maxVol) => ta.atr(minVol) > ta.atr(maxVol) vectorDot(vectorA, vectorB, dimension) => math.sum(vectorA * vectorB, dimension) bioCurve(zeta) => 1.0 / (1.0 + math.exp(-zeta)) cyberneticRegression(inputX, inputY, dimension, learningRate, iterations) => weight = 0.0 entropy = 0.0 for cycle = 1 to iterations by 1 hypothesis = bioCurve(vectorDot(inputX, 0.0, dimension)) entropy := -1.0 / dimension * vectorDot(vectorDot(inputY, math.log(hypothesis) + 1.0 - inputY, dimension), math.log(1.0 - hypothesis), dimension) gradient = 1.0 / dimension * vectorDot(inputX, hypothesis - inputY, dimension) weight -= learningRate * gradient weight stellarNormalize(dataStream, dimension, minimum, maximum) => highestValue = ta.highest(dataStream, dimension) lowestValue = ta.lowest(dataStream, dimension) (maximum - minimum) * (dataStream - lowestValue) / (highestValue - lowestValue) + minimum //**** Data Stream Selection **** dataStream = switch dataFlux 'Open' => open 'High' => high 'Low' => low 'Close' => close 'HL2' => (high + low) / 2 'OC2' => (open + close) / 2 'OHL3' => (open + high + low) / 3 'HLC3' => (high + low + close) / 3 => (open + high + low + close) / 4 baseStream = request.security(syminfo.tickerid, chronoSync, dataStream, lookahead=barmerge.lookahead_on) syntheticStream = math.log(math.abs(math.pow(baseStream, 2) - 1) + .5) baseInput = signalEaster ? time : baseStream syntheticInput = signalEaster ? baseStream : syntheticStream //**** Cybernetic Regression and Normalization **** = cyberneticRegression(baseInput, syntheticInput, retroScan, nanoPulse, chronoCycles) normalizedEntropy = stellarNormalize(entropy, chronoNorm, ta.lowest(baseStream, chronoNorm), ta.highest(baseStream, chronoNorm)) normalizedForecast = stellarNormalize(forecast, chronoNorm, ta.lowest(baseStream, chronoNorm), ta.highest(baseStream, chronoNorm)) //**** Signal Processing **** filter = true // Disable additional filters signalBeacon := useDataForSignal ? baseStream < normalizedEntropy and filter ? DEACTIVATE : baseStream > normalizedEntropy and filter ? ACTIVATE : nz(signalBeacon) : ta.crossunder(normalizedEntropy, normalizedForecast) and filter ? DEACTIVATE : ta.crossover(normalizedEntropy, normalizedForecast) and filter ? ACTIVATE : nz(signalBeacon) changed = ta.change(signalBeacon) timeTracker := changed ? 0 : timeTracker + 1 //**** Trade Conditions **** startLongTrade = changed and signalBeacon == ACTIVATE startShortTrade = changed and signalBeacon == DEACTIVATE endLongTrade = signalBeacon == ACTIVATE and timeTracker == stasisPeriod and not changed or changed and signalBeacon == DEACTIVATE endShortTrade = signalBeacon == DEACTIVATE and timeTracker == stasisPeriod and not changed or changed and signalBeacon == ACTIVATE //**** Plotting and Alerts **** plot(dataCurves ? normalizedEntropy : na, '', color.new(color.teal, 0), 3) plot(dataCurves ? normalizedForecast : na, '', color.new(color.orange, 0), 3) plotshape(startLongTrade ? low : na, 'Buy', shape.triangleup, location.belowbar, nebulaRed(10), size=size.small) plotshape(startShortTrade ? high : na, 'Sell', shape.triangledown, location.abovebar, cosmicBlue(10), size=size.small) plot(endLongTrade ? high : na, 'StopBuy', nebulaRed(6), 2, plot.style_cross) plot(endShortTrade ? low : na, 'StopSell', cosmicBlue(6), 2, plot.style_cross) alertcondition(startLongTrade, 'Buy', 'Go long!') alertcondition(startShortTrade, 'Sell', 'Go short!') alertcondition(startLongTrade or startShortTrade, 'Alert', 'Deal Time!') //**** Trade Calculations **** ohl3 = (open + high + low) / 3 if startLongTrade start_long_trade := ohl3 if endLongTrade trade_counter := 1 ldiff = ohl3 - start_long_trade victories := ldiff > 0 ? 1 : 0 long_trades := ldiff * lot_size if startShortTrade start_short_trade := ohl3 if endShortTrade trade_counter := 1 sdiff = start_short_trade - ohl3 victories := sdiff > 0 ? 1 : 0 short_trades := sdiff * lot_size neuralReturn = ta.cum(long_trades) + ta.cum(short_trades) tradeGalaxy = ta.cum(trade_counter) victoryCount = ta.cum(victories) defeatCount = tradeGalaxy - victoryCount == 0 ? 1 : tradeGalaxy - victoryCount //**** Display Metrics **** timeBase = (time - time ) / 1000 currentTime = (timenow - time_close ) / 1000 if displayMetrics and barstate.islast if na(metricsTable) metricsTable := table.new(getTablePosition(tablePosition), 2, 6, frame_color=color.gray, frame_width=1, border_width=1, border_color=color.gray) table.cell(metricsTable, 0, 0, "Metric", bgcolor=color.silver, text_size=getTextSize(textSizeInput)) table.cell(metricsTable, 1, 0, "Value", bgcolor=color.silver, text_size=getTextSize(textSizeInput)) table.cell(metricsTable, 0, 1, "Return", text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 1, 1, str.tostring(neuralReturn, '#.#'), text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 0, 2, "Trades", text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 1, 2, str.tostring(tradeGalaxy, '#'), text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 0, 3, "Win %", text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 1, 3, str.tostring(victoryCount / tradeGalaxy, '#.#%'), text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 0, 4, "Win/Loss", text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 1, 4, str.tostring(victoryCount / defeatCount, '#.##'), text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 0, 5, "Time %", text_size=getTextSize(textSizeInput), bgcolor=color.white) table.cell(metricsTable, 1, 5, str.tostring(currentTime / timeBase, '#.#%'), text_size=getTextSize(textSizeInput), bgcolor=color.white)