Technical Analysis (TA) for Traders involves studying past market data—primarily price and volume—to identify patterns and forecast future price movements. Traders use TA before trading financial assets such as stocks and cryptocurrencies, where markets are often volatile and influenced by various external factors. For extensive backgrounds see Investopedia.
At code2trade.dev, we apply TA extensively using Python libraries and custom implementations. Our goal is to pinpoint trading signals and predict price movements effectively. Below, we outline TA principles in general and how we specifically put them to use.
Core Principles of Technical Analysis for Traders
- Price Discounts Everything:
- TA assumes that all relevant market information (economic factors, news, etc.) is already reflected in the price.
- Price Moves in Trends:
- Markets exhibit trends (upward, downward, or sideways) that can be leveraged for trading decisions.
- History Repeats Itself:
- Market patterns, driven by human psychology, tend to repeat over time, creating recognizable setups.
TA Techniques Used at code2trade.dev
1. Moving Averages (MA) and Exponentially Weighted Moving Averages (EMA)
- Purpose: Smooth out price data to identify trends.
- Application:
- We compute 7-day, 20-day, and 50-day MAs and EMAs to detect bullish and bearish crossovers.
- Signals:
- Buy: When shorter MAs cross above longer MAs.
- Sell: When shorter MAs cross below longer MAs.
2. Bollinger Bands
- Purpose: Measure price volatility and identify overbought or oversold conditions.
- Application:
- We calculate Bands using a moving average and a standard deviation multiplier.
- Entry Signal: When price approaches the lower band.
- Exit Signal: When price nears the upper band.
3. Relative Strength Index (RSI)
- Purpose: Gauge momentum and overbought/oversold levels.
- Application:
- We use a 14-period RSI with thresholds:
- RSI > 70: Overbought (sell signal).
- RSI < 30: Oversold (buy signal).
- Custom thresholds like 34 (entry) and 84 (exit) fine-tune our strategy for specific market conditions.
- We use a 14-period RSI with thresholds:
4. Schaff Trend Cycle (STC) Indicator
- Purpose: Identify bullish or bearish trends with sensitivity to cycles.
- Application:
- STC oscillates between 0 and 100:
- Values > 73 suggest potential bullish trends.
- Values < 97 indicate bearish trends.
- Combined with other indicators for entry/exit validation.
- STC oscillates between 0 and 100:
5. Gaussian Filters
- Purpose: Smooth price data while identifying peaks and valleys for trading signals.
- Application:
- Gaussian filters help refine signal accuracy by reducing noise in the data.
- Used to detect pivot points (peaks for selling, valleys for buying).
6. Keltner Channels
- Purpose: Combine volatility and moving averages for trend identification.
- Application:
- We generate Signals when prices cross the upper or lower channel boundaries.
- Validates trends in conjunction with Bollinger Bands.
Implementation Approach TA for Traders
- Data Handling:
- We retrieve Price and volume data from APIs like Binance and process them into clean dataframes.
- We leverage TA libraries such as
pandas_taandtafor indicator calculations.
- Signal Generation:
- We calculated indicators like RSI, Bollinger Bands, and STC for entire datasets.
- We generate entry and exit signals based on specific thresholds and conditions.
- Strategy Execution:
- We combine signals from multiple indicators (e.g., RSI, STC, Bollinger Bands) for robust decision-making.
- Buy/Sell signals require at least two confirmations from different indicators.
- Visualization:
- We use Plotly to visualize price data, trading signals, and indicators (e.g., candlestick charts, volume overlays).
- Interactive charts allow zooming into specific timeframes for detailed analysis.
- Automation:
- Automated scripts handle data retrieval, indicator computation, signal generation, and visualization.
- Gaussian filters and statistical analyses further refine predictions.