Break or Fake?: We'll know soon.

позавчера

I see social media awash with the posts about the BTC breakout.

We are close. Very close to a legit breakout and if it comes, I'll retract a good amount of the ill talk I did on the halving theory (Not all of it, I still think assuming flawless cycles on 2 events is silly) - if I am wrong, I'll change my stance.

However, if this is going to fail we will be into the final baiting of the bulls now.

Ugly stuff if we reject.

And if we reject, then the STF and halving theories fail. A failure of these would probably result in the biggest retail loss of all time.

Lot of people heavily betting this is a fact and not a hypothesis.

позавчера

Комментарий

Social media live feed.

вчера

Комментарий

Reloaded on my deep OTM COIN puts.

15 часов назад

Комментарий

Lot of people wanting to tell me this is a bullish pattern and not a bearish one. My point is if you check the pattern hit rate, it's actually close to a coin flip.

There's no reason to be confident to either side. The best thing to do is buy the extreme lows if bullish, short the extreme highs if bearish and set good stops. Then you have 1:3 RR on a setup that has the min odds of 40% of working. Which is a trading edge.

15 часов назад

Комментарий

Last time there was a failed instance of this type of shape for a BTC bull move we dropped 75%.

That's the known risk based on the charting history.

11 часов назад

Комментарий

BTC is at the 76 retracement of the full drop. Statistically the riskiest time to be bullish.

Usually you're wrong or you can buy the same price on a retest.

Like, over 85% of time one of those things happens.

11 часов назад

Комментарий

^^ Which is a far higher hit rate than any variant of bullish flag like pattern.

Significantly higher.

7 часов назад

Комментарий

Here's stats for an automated test of bull flags on the daily chart in BTC since 2015.

Bull Flag Pattern P&L Stats in BTC:

- Total Bull Flags: 1602
- Wins: 576
- Losses: 1026
- Win Rate: 36.0%
- Average Gain per Flag: -2.11%
- Total Gain: -3373.25%

--
This was on settings people use quite often. Code attached if you want to test your own settings.
--
import yfinance as yf
import pandas as pd

# Parameters for bull flag detection
PULLBACK_DAYS = 5
BREAKOUT_DAYS = 5
WIN_THRESHOLD = 0 # 0% return or higher is considered a "win"

def download_data(ticker="BTC-USD", start="2015-01-01", end="2023-12-31"):
"""Download BTC daily data from Yahoo Finance"""
data = yf.download(ticker, start=start, end=end)
return data

def detect_bull_flags(data, pullback_days=PULLBACK_DAYS, breakout_days=BREAKOUT_DAYS):
"""Detect bull flags and calculate post-breakout performance"""
flags = []
data['High_Close'] = data['Close'].rolling(pullback_days).max()

for i in range(pullback_days, len(data) - breakout_days):
pullback_data = data['Close'].iloc[i-pullback_days:i]
if (pullback_data[-1] < pullback_data.max()) and (pullback_data.min() < pullback_data[-1]):
breakout_level = data['High_Close'].iloc
post_breakout_data = data['Close'].iloc[i:i + breakout_days]
breakout_gain = (post_breakout_data[-1] - breakout_level) / breakout_level
flags.append({
'Date': data.index,
'Breakout_Gain (%)': breakout_gain * 100,
'Win': breakout_gain > WIN_THRESHOLD
})

return pd.DataFrame(flags)

def display_pl_stats(bull_flags):
"""Display win/loss and P&L statistics for bull flags."""
wins = bull_flags['Win'].sum()
losses = len(bull_flags) - wins
win_rate = (wins / len(bull_flags)) * 100
avg_gain = bull_flags['Breakout_Gain (%)'].mean()
total_gain = bull_flags['Breakout_Gain (%)'].sum()

print("Bull Flag Pattern P&L Stats in BTC:\n")
print(f"- Total Bull Flags: {len(bull_flags)}")
print(f"- Wins: {wins}")
print(f"- Losses: {losses}")
print(f"- Win Rate: {win_rate:.1f}%")
print(f"- Average Gain per Flag: {avg_gain:.2f}%")
print(f"- Total Gain: {total_gain:.2f}%")

def main():
# Step 1: Download Data
data = download_data()

# Step 2: Detect Bull Flags
bull_flags = detect_bull_flags(data)

# Step 3: Display P&L Stats
display_pl_stats(bull_flags)

if __name__ == "__main__":
main()

7 часов назад

Комментарий

It's not as scary to bet against if you check the numbers. Odds are not anywhere near as bad as many are presenting them to be for bears.

7 часов назад

Комментарий

If you'd bet on all instances of a bull flag in BTC on the daily chart since 2015 when we were at all time high you'd have been down. You'd be down more now.

This is the performance of the pattern we're discussing.

7 часов назад

Комментарий

Here's a code that will look for patterns broadly the same as this in BTC history and show you a chart of the time and what happened next. Sometimes it pops. Others it goes sideway. And sometimes it slams. To be profitable with the pattern, you'd have to be very good with your stop loss and take profit.

Here's the net stats:
BTC Pattern Detection Statistics:
- Total Patterns Detected: 137
- Average Volatility During Consolidation: 0.17
- Average Outcome Gain After Pattern: 1.11%
- Win Rate (Positive Outcome): 57.66%

7.66% above random. Given that the move overall has favoured the upside - this should be considered a meaningless result. It's random. Tiny tiny edge to bulls.

It's a coin flip.
Here's a sample of different outcomes.



--
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Parameters for pattern detection
UPWARD_MOVE_DAYS = 3 # Number of days for initial upward move
UPWARD_MOVE_THRESHOLD = 0.1 # Minimum % gain in upward move (e.g., 10% increase)
CONSOLIDATION_DAYS = 10 # Number of days for sideways consolidation
VOLATILITY_THRESHOLD = 0.02 # Minimum % range in consolidation to indicate volatility
POST_PATTERN_DAYS = 5 # Number of days to show after the pattern for outcome

def download_data(ticker="BTC-USD", start="2015-01-01", end="2023-12-31"):
"""Download BTC daily data from Yahoo Finance"""
data = yf.download(ticker, start=start, end=end)
return data

def detect_pattern(data):
"""Detects the upward move followed by volatile sideways consolidation"""
patterns = []

for i in range(UPWARD_MOVE_DAYS, len(data) - CONSOLIDATION_DAYS - POST_PATTERN_DAYS):
# Step 1: Detect significant upward move
initial_move = data['Close'].iloc[i - UPWARD_MOVE_DAYS:i]
move_pct = (initial_move[-1] - initial_move[0]) / initial_move[0]

if move_pct >= UPWARD_MOVE_THRESHOLD:
# Step 2: Check for volatile sideways consolidation
consolidation_period = data['Close'].iloc[i:i + CONSOLIDATION_DAYS]
price_range = consolidation_period.max() - consolidation_period.min()
volatility = price_range / consolidation_period.mean()

if volatility >= VOLATILITY_THRESHOLD:
patterns.append({
'Start_Date': data.index[i - UPWARD_MOVE_DAYS],
'End_Date': data.index[i + CONSOLIDATION_DAYS - 1],
'Outcome_End_Date': data.index[i + CONSOLIDATION_DAYS + POST_PATTERN_DAYS - 1]
})

return patterns

def plot_patterns(data, patterns):
"""Plots each detected pattern on a zoomed-in chart with outcome"""
for pattern in patterns:
start, end, outcome_end = pattern['Start_Date'], pattern['End_Date'], pattern['Outcome_End_Date']
pattern_data = data.loc[start:outcome_end]

plt.figure(figsize=(12, 6))
plt.plot(pattern_data['Close'], color='gray', label="BTC Price", alpha=0.5)
plt.plot(data['Close'].loc[start:end], color='blue', label="Detected Pattern")
plt.axvline(x=end, color='red', linestyle='--', label="Pattern End")
plt.title(f"BTC Pattern from {start.date()} to {end.date()} (Outcome Shown)")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.show()

def main():
# Step 1: Download BTC Data
data = download_data()

# Step 2: Detect Patterns
patterns = detect_pattern(data)

# Step 3: Plot Patterns
if patterns:
plot_patterns(data, patterns)
else:
print("No patterns detected.")

if __name__ == "__main__":
main()

--

6 часов назад

Комментарий

People who tell you this is a high probability bull setup in BTC have not done the work. They've looked for times BTC popped and think they know a bullish pattern.

They've not checked all instances of that pattern forming and how it would look/feel in real time in the false ones.

The hit rate of these things is crazily overstated.

You can't make money trading these without good stops and targets. Anyone saying you can, has not tried it.

3 часа назад

Комментарий

This is the retest of the 76. Should sell soon if the 76 is resis.