some interesting metricsi will have ai digging through the 4chan archives next and will have it plot a pnl of notable posts we can take this deeper but i need metric ideas
26/185 isn't notable, considering how overrepped they are in "journalism".
>>61453363 it is when you consider some have made multiple death call outs peter schiff has made 10 alone
>>61453356omg i can turn jes in to a trading metric. will post proof shortly
>>61453435jews*
Betting against the Jews has literally NEVER worked in anyone's favour since they came to exist.
This is the future, retards making an AI hallucinate random bullshit then saying this is undeniable proof
>>61453452actually..... trading again the jews has the most profit
>>61453356>>61453368>jews bad, right fellow chuds?
>>61453589 not " bad" more like an "oy ve " indicator of price movement
>>61453589absolutely correct schlomo, go bit a childs penis
i made a python script that pulls from Jew Bitcoin posters across x it aggrragtes and filters posts and sentiment , if this shit hits mid December i want the entire boad FILLED WITH DRAYDELS for thumbnails screenshot this import pandas as pdimport numpy as npdef compute_zlema(close, period=14): ema1 = close.ewm(span=period).mean() adjusted = 2 * close - close.shift(1) return adjusted.ewm(span=period).mean()def fractal_bottom(low): lows = low.values bottoms = np.zeros(len(lows)) for i in range(2, len(lows)-2): if lows[i] < lows[i-2:i].min() and lows[i] < lows[i+1:i+3].min(): bottoms[i] = 1 return pd.Series(bottoms, index=low.index)def reversal_indicator(df, deaths_col='total_deaths', window=20, z_thresh=-2, vol_thresh=1): df = df.copy() df['zlema'] = compute_zlema(df['close']) df['dev_z'] = (df['close'] - df['zlema']) / df['zlema'].rolling(window).std() df['deaths_z'] = (df[deaths_col] - df[deaths_col].rolling(window).mean()) / df[deaths_col].rolling(window).std() df['custom_z'] = 0.7 * df['dev_z'] + 0.3 * (-df['deaths_z']) df['fractal_bottom'] = fractal_bottom(df['low']) df['vol_z'] = (df['volume'] - df['volume'].rolling(window).mean()) / df['volume'].rolling(window).std() df['signal'] = ((df['custom_z'] < z_thresh) & (df['fractal_bottom'] == 1) & (df['vol_z'] > vol_thresh)).astype(int) return df# Usage: Load your DF with 'close', 'low', 'volume', 'total_deaths'# df = reversal_indicator(df)# signals = df[df['signal'] == 1]probably needs refinement (ai be line that)
>>61453725Thanks, OP. Good confirmation there.
dont let the jews plot against you without an indicator. ai is the future it can turn any concept into data proven indicators for reconstruction sake you can put this in grok 4.1 thinking mode and tell it to look up the pinescript v6 migration guide and to look up how to fix issues with the pinecoders website.tell it no external api allow in pinescript and on like the 3rd or 4th debug version (just feed it back the error codes ) it will spit out a working indicator for transparency im giving how and what the indicator does ask grok to explain or refine it using phd level deeper search mode
>>61453589Duh
>>61453589Y... ya.. they are.
>>61453949Can you repost this image as png?
>>61453356I was gonna talk shit but 26/185 is kinda a lot
