Understanding Bitcoin price action through Technical Analysis | by ab1sh3k | The Capital | Mar, 2025
Understanding Bitcoin through technical analysis is somewhat akin to predicting human behavior purely through statistical probabilities — it’s intellectually tantalizing, highly informative, yet seldom foolproof. Imagine employing rigorous statistical analysis to decode precisely how a guest might behave at next weekend’s dinner party; no matter how sophisticated your mathematical tools are, human unpredictability often reigns supreme. Bitcoin, with its blend of human emotion, technological complexity, and raw economic ambition, behaves similarly. Technical analysis thus becomes an elegant quest, a daring attempt to make sense out of apparent chaos.
import pandas as pd
import numpy as np
np.NaN = np.nan # Add this line for pandas_ta compatibility
import matplotlib.pyplot as plt
import requests
from datetime import datetime, timedelta
import pandas_ta as ta
import mplfinance as mpf
import warnings
warnings.filterwarnings('ignore')class BitcoinTechnicalAnalysis:
def __init__(self):
"""Initialize the class with empty data"""
self.data = None
self.api_url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart"
def fetch_bitcoin_data(self, days=365, vs_currency='usd'):
"""Fetch Bitcoin price data from CoinGecko API"""
try:
params = {
'vs_currency': vs_currency,
'days': days,
'interval': 'daily'
}
response = requests.get(self.api_url, params=params)
response.raise_for_status()
data = response.json()
# Create DataFrame from API response
df = pd.DataFrame({
'Timestamp': [x[0] for x in data['prices']],
'Close': [x[1] for x in data['prices']],
'Volume': [x[1] for x in data['total_volumes']]
})
# Convert timestamp to datetime
df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms')
df.set_index('Timestamp', inplace=True)
# Add High and Low prices (approximated from Close)
df['High'] = df['Close'] * 1.02
df['Low'] = df['Close'] * 0.98
df['Open'] = df['Close'].shift(1)
self.data = df.dropna()
print(f"Successfully fetched {len(self.data)} days of Bitcoin data")
return self.data
except requests.exceptions.RequestException as e:
print(f"Error fetching data: {e}")
return None
def calculate_indicators(self):
"""Calculate all technical indicators"""
if self.data is None:
print("No data available. Please fetch data first.")
return None
df = self.data.copy()
print("Calculating technical indicators...")
# Price Trend Indicators
df['SMA7'] = ta.sma(df['Close'], length=7)
df['SMA25'] = ta.sma(df['Close'], length=25)
df['SMA50'] = ta.sma(df['Close'], length=50)
df['SMA99'] = ta.sma(df['Close'], length=99)
df['SMA200'] = ta.sma(df['Close'], length=200)
df['EMA12'] = ta.ema(df['Close'], length=12)
df['EMA26'] = ta.ema(df['Close'], length=26)
df['MA111'] = ta.sma(df['Close'], length=111)
df['MA350x2'] = ta.sma(df['Close'], length=350) * 2
macd = ta.macd(df['Close'], fast=12, slow=26, signal=9)
df['MACD'] = macd['MACD_12_26_9']
df['MACD_Signal'] = macd['MACDs_12_26_9']
df['MACD_Hist'] = macd['MACDh_12_26_9']
df['SAR'] = ta.psar(df['High'], df['Low'], df['Close'])['PSARl_0.02_0.2']
# Momentum Indicators
df['RSI'] = ta.rsi(df['Close'], length=14)
stoch = ta.stoch(df['High'], df['Low'], df['Close'], k=14, d=3, smooth_k=3)
df['StochK'] = stoch['STOCHk_14_3_3']
df['StochD'] = stoch['STOCHd_14_3_3']
# Volatility Indicators
bbands = ta.bbands(df['Close'], length=20, std=2)
df['BB_Upper'] = bbands['BBU_20_2.0']
df['BB_Middle'] = bbands['BBM_20_2.0']
df['BB_Lower'] = bbands['BBL_20_2.0']
df['CCI'] = ta.cci(df['High'], df['Low'], df['Close'], length=14)
# Volume Indicators
df['OBV'] = ta.obv(df['Close'], df['Volume'])
df['CMF'] = ta.adosc(df['High'], df['Low'], df['Close'], df['Volume'], fast=3, slow=10)
df['ForceIndex'] = df['Close'].diff(1) * df['Volume']
df['ForceIndex13'] = ta.ema(df['ForceIndex'], length=13)
# Additional indicators
df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)
recent_high = df['High'].iloc[-100:].max()
recent_low = df['Low'].iloc[-100:].min()
df['Fib_0'] = recent_low
df['Fib_23.6'] = recent_low + 0.236 * (recent_high - recent_low)
df['Fib_38.2'] = recent_low + 0.382 * (recent_high - recent_low)
df['Fib_50'] = recent_low + 0.5 * (recent_high - recent_low)
df['Fib_61.8'] = recent_low + 0.618 * (recent_high - recent_low)
df['Fib_100'] = recent_high
self.data = df
print("Indicators calculated successfully")
return df
if __name__ == "__main__":
btc = BitcoinTechnicalAnalysis()
btc.fetch_bitcoin_data(days=365)
indicators = btc.calculate_indicators()
if indicators is not None:
print(indicators.tail())
At its heart, technical analysis involves meticulously examining historical market data to forecast potential future price movements. For Bitcoin, this equates to combing through past price actions, searching carefully for patterns, repetition, and hints of the crypto’s future temperament. Think of yourself as a detective, but instead of fingerprints and CCTV footage, your clues lie in neatly structured datasets, mathematical patterns, and calculated probabilities. This detective work hinges upon both rigorous mathematical methods and insightful, nuanced intuition — like mixing Sherlock Holmes with a dash of Albert Einstein, sprinkled generously with some philosophical introspection.
Let’s paint a vivid analogy. Imagine attending an exclusive social gathering where Bitcoin represents the enigmatic guest whom everyone speaks about — often inaccurately — but nobody truly knows. People make grand assumptions, wild guesses, and even hasty conclusions about its behavior. Your task, as the well-informed observer, is to calmly decipher the reality behind the rumors. To perform this feat, you’re armed not with gossip or hearsay, but with disciplined analytical techniques, precise Python scripts, robust statistical formulas, and the philosophical patience of a wise scholar.
Your Python script embarks on this journey by courteously engaging the CoinGecko API. Picture the API as your personal informant, always ready and reliable, dutifully providing historical Bitcoin data — complete with daily prices, fluctuations, and the significant highs and lows spanning the past year. CoinGecko, in this sense, becomes your trusted oracle, generously sharing data points which you meticulously capture and organize within a sleek, structured DataFrame. This DataFrame acts like your digital notebook, a systematic, detailed record of Bitcoin’s every price hiccup and triumph. It documents periods of exhilaration, cautious hesitations, sudden panics, and graceful recoveries — capturing a narrative as compelling as any human story.
Once armed with this thoroughly curated price diary, the genuine thrill begins: calculating technical indicators. If Bitcoin is the mysterious party guest, these indicators represent your sophisticated tools of behavioral decoding — translating market psychology into mathematical form. Indicators transform numerical data into meaningful psychological insights, bridging cold, hard facts with nuanced emotional undertones. They communicate stories of collective investor optimism, anxiety, euphoria, and even outright fear.
First, consider the venerable Simple Moving Average (SMA). It functions like a gentle sage — calm, patient, and insightful — carefully smoothing out the sharp, erratic edges of Bitcoin’s day-to-day volatility. It reflects broader sentiment rather than transient excitement, offering a balanced perspective of Bitcoin’s long-term intentions. The SMA, calculated by averaging prices over a set period, graciously filters short-term noise and emotions, providing you clarity and objectivity — qualities especially precious in turbulent crypto markets.
Yet, its energetic sibling, the Exponential Moving Average (EMA), boldly emphasizes recent price data. The EMA is less the wise elder, more the astute young analyst, sharply attuned to Bitcoin’s freshest movements. It assigns greater significance to recent price action, acting swiftly and responsively — perfect for traders who crave up-to-the-minute insights with their morning coffee. If the SMA whispers wisdom softly from afar, the EMA shouts relevant advice eagerly, helping traders adapt nimbly to Bitcoin’s whims.
Then, comes the Moving Average Convergence Divergence (MACD) — an indicator both rhythmic and dramatic, echoing the very heartbeat of Bitcoin’s sentiment. Imagine a heartbeat monitor charting emotional peaks and valleys; similarly, the MACD chronicles Bitcoin’s speed and momentum. When MACD lines dramatically cross paths, Bitcoin metaphorically experiences an emotional epiphany — a potential signal that the market might soon pivot direction. It’s akin to noticing a friend’s sudden pause or abrupt enthusiasm shift mid-conversation, alerting you to underlying changes in mood or perspective.
Another intriguing indicator is the cryptically-named Bollinger Bands. Visualize Bitcoin gently bouncing within elastic bands that expand and contract according to the market’s volatility level. Wider Bollinger Bands signal excitement or agitation — Bitcoin is feeling adventurous or anxious. Narrower bands hint at investor boredom, uncertainty, or a cautious wait-and-see attitude — Bitcoin patiently awaiting its next significant move. Thus, Bollinger Bands illustrates the market’s collective psyche in vivid graphical elegance, capturing volatility visually and intuitively.
Equally poetic is the Fibonacci retracement. Derived from Fibonacci’s mystical sequence (a mathematical phenomenon found throughout nature, art, and even cosmic arrangements), Fibonacci retracement levels appear deceptively simple yet powerfully insightful. By applying Fibonacci ratios to price charts, traders identify likely areas of psychological resistance or support. It feels like tapping into a hidden rhythm, a secret harmonic resonance underlying seemingly random market movements. Indeed, Bitcoin’s price frequently pauses at these retracement levels, as if taking a momentary breath or indulging in reflective contemplation before surging forward or pulling back. Even cryptos pause occasionally for existential introspection.
Beyond price alone lies the powerful dimension of trading volume. Volume-based indicators offer a fascinating glimpse behind Bitcoin’s price curtain, examining underlying investor conviction. The On-Balance Volume (OBV) methodically accumulates daily volume, rising during bullish enthusiasm, falling during bearish anxiety. Imagine overhearing private conversations among party guests, gleaning deeper insights beyond surface-level interactions. OBV reveals whether Bitcoin’s latest price moves carry genuine conviction or lack true strength — a critical distinction for nuanced market understanding.
Similarly, Chaikin Money Flow (CMF) elegantly quantifies money moving into or out of Bitcoin over specific intervals. CMF captures the intensity and enthusiasm of investor participation, akin to hearing crowd murmurs intensify or quiet down around Bitcoin’s metaphorical party corner. Positive CMF indicates strong buying enthusiasm, while negative readings suggest mounting selling pressure. Like a sensitive microphone capturing hushed whispers or boisterous cheers, CMF elegantly reveals market sentiment beneath surface-level appearances.
The Force Index blends volume with price momentum, providing another insightful dimension. It combines daily price changes with transaction volume, translating Bitcoin’s emotional energy into quantifiable form. A forceful upward move in the Force Index signals enthusiastic buying; conversely, significant downward spikes indicate emotional selling panic. It acts as a market psychology barometer, effortlessly quantifying Bitcoin’s emotional temperature — a vital gauge in your analytical toolkit.
Average True Range (ATR), meanwhile, complements these indicators beautifully. It quantifies recent volatility, providing a straightforward yet crucial assessment: is Bitcoin currently tranquil and steady, or wildly adventurous? High ATR values indicate heightened price fluctuation — Bitcoin energetically zigzagging amid investor excitement. Conversely, low ATR values signal calmer, quieter times — Bitcoin maintaining composed decorum. ATR adds yet another valuable psychological lens, transforming raw volatility data into meaningful market narratives.
When Python’s powerful numerical calculations meet technical analysis, these diverse indicators coalesce into rich, meaningful market stories. The script transforms intimidating arrays of numbers into engaging narratives, illuminating Bitcoin’s complex personality. Each indicator thus becomes your conversational companion, whispering thoughtful insights into Bitcoin’s moods, impulses, and even its whimsical personality quirks.
Ultimately, understanding Bitcoin’s personality through technical analysis resembles conversing with an intellectually stimulating, slightly eccentric friend: unpredictable yet reliably intriguing. While no Python script can transform anyone into a prophetic clairvoyant, technical analysis equips traders with sophisticated frameworks and disciplined perspectives, helping navigate uncertainty gracefully. The combined insights from indicators enhance your intuitive understanding, providing greater confidence in otherwise chaotic crypto waters.
Therefore, next time Bitcoin arises casually in conversation or news headlines flash sudden volatility alerts, remember this sophisticated interplay: historical patterns, mathematical elegance, investor psychology, innovative technology — all harmonizing subtly within Bitcoin’s seemingly chaotic price moves. What initially seems unpredictable slowly becomes comprehensible, even elegant.
Indeed, Bitcoin ceases to be simply a mysterious stranger, morphing gradually into a familiar acquaintance whose eccentric behaviors, while never fully predictable, become comfortably understandable and even endearing. After all, technical analysis doesn’t merely analyze price charts; it lovingly narrates a living, breathing story — transforming Bitcoin from intimidating mystery into a charming enigma you confidently grasp, appreciate, and perhaps even cherish.
