Technical Analysis Basics for Cryptocurrency Trading

Technical analysis examines historical price and volume data to identify patterns and potential future movements. While controversial and far from foolproof, technical analysis provides a common language for discussing markets.

Core Assumptions

Technical analysis rests on several assumptions: prices reflect all available information, prices move in trends, and history tends to repeat through recurring patterns.

These assumptions are debatable. Cryptocurrency markets show unusual characteristics that may violate traditional technical analysis assumptions. However, many participants use technical analysis, creating self-fulfilling aspects.

Chart Types

Candlestick charts show open, high, low, and close prices for each period. Green or white candles indicate closes above opens; red or black indicate closes below opens.

Candlestick patterns like doji, hammer, and engulfing patterns supposedly indicate trend reversals or continuations. However, patterns show high false positive rates.

Line charts connect closing prices, providing cleaner but less detailed views. Bar charts show high, low, open, and close similar to candlesticks but in different format.

Support and Resistance

Support levels are prices where buying pressure historically exceeded selling pressure, preventing further declines. Resistance levels are where selling pressure exceeded buying pressure, preventing further increases.

These levels matter because many traders place orders near historical support and resistance, creating self-fulfilling behavior.

However, support and resistance are zones rather than precise prices. Expecting exact bounces leads to disappointment.

Trendlines

Trendlines connect swing lows in uptrends or swing highs in downtrends. They supposedly show trend direction and provide support or resistance.

Trendline breaks might signal trend reversals. However, which points to connect when drawing trendlines is subjective. Different analysts draw different lines on the same chart.

Moving Averages

Moving averages smooth price data by averaging prices over specific periods. The 50-day and 200-day moving averages are commonly watched.

Prices above moving averages suggest uptrends; below suggests downtrends. Moving average crossovers - faster averages crossing slower averages - supposedly signal trend changes.

However, moving averages lag price. By the time averages confirm trends, significant movement has occurred. They describe what happened more than predict what will happen.

Momentum Indicators

Relative Strength Index (RSI) measures price change magnitude on a scale from 0 to 100. Above 70 suggests overbought conditions; below 30 suggests oversold.

However, strong trends can remain overbought or oversold for extended periods. RSI divergences - price making new highs while RSI doesn't - supposedly signal weakening momentum.

Moving Average Convergence Divergence (MACD) shows relationship between two moving averages. MACD line crossing signal line supposedly indicates buy or sell signals.

Volume Analysis

Volume indicates conviction behind price movements. Price increases on high volume suggest strong buying; on low volume suggest weak moves likely to reverse.

Volume precedes price sometimes. Rising volume while prices consolidate might indicate accumulation before upward breakouts.

However, cryptocurrency volume data is problematic. Wash trading inflates reported volumes, making reliable volume analysis difficult.

Chart Patterns

Head and shoulders patterns supposedly signal trend reversals. Double tops and bottoms similarly indicate reversals. Triangles, flags, and pennants indicate continuation patterns.

These patterns show high failure rates. Identifying them in real-time (not retrospectively) is difficult. Most patterns become obvious only after breakouts occur.

Fibonacci Retracements

Fibonacci levels (23.6%, 38.2%, 61.8%) are drawn between swing highs and lows. Prices supposedly find support or resistance near these levels.

Fibonacci analysis is controversial even among technical analysts. However, many traders watch these levels, potentially creating self-fulfilling behavior.

Timeframes

Different timeframes show different patterns. Weekly and daily charts reveal longer-term trends. Hourly and minute charts show short-term movements.

Conflicting signals across timeframes create confusion. Uptrend on daily chart might show downtrend on hourly chart. Experienced traders consider multiple timeframes.

Limitations in Cryptocurrency

Cryptocurrency markets pose challenges for technical analysis:

24/7 trading means no daily opens and closes in traditional sense. Weekend gaps don't exist.

Low liquidity in many cryptocurrencies allows manipulation through chart painting - creating technical patterns deliberately to trap technical traders.

Relatively short history means less data for validating patterns compared to stock markets.

Extreme volatility causes frequent false signals from indicators designed for calmer markets.

Backtesting

Backtesting applies technical strategies to historical data to evaluate performance. However, optimizing strategies on historical data often creates curve-fitted systems that fail in real trading.

Cryptocurrency backtesting is particularly problematic due to limited historical data and changing market characteristics.

Combining Indicators

Using multiple indicators might provide confirmation, but too many indicators create conflicting signals and analysis paralysis.

Indicators tend to correlate - most measure momentum or trend in related ways. Adding more indicators rarely provides truly independent confirmation.

Self-Fulfilling Aspects

Technical analysis works partially because many traders use it. If enough participants place buy orders at a support level, that level holds due to the buying itself.

This creates feedback loops. However, when too many participants expect the same outcome, contrarian moves become profitable for large players.

Conclusion

Technical analysis provides framework for discussing price movements and identifying potential trades. However, evidence for consistent profitability from pure technical trading is weak. Use technical analysis as one input among many, not as a complete trading system. Focus on risk management and position sizing at least as much as on signal generation.