Technical Analysis with Python for Algorithmic Trading

Learn to install Python and libraries with Anaconda, set up and use Jupyter notebooks, and ensure an updated Anaconda installation to avoid errors.

Install the Anaconda distribution to get Python with a complete data science environment, preinstalled packages, and a package manager that simplifies dependencies and supports Jupyter notebooks, PyCharm, and Spyder.

Use the Anaconda Navigator to launch Jupyter Notebook and Jupyter Lab. Explore managing environments and running code with simple Python operations, including keyboard shortcuts like shift-enter and alt-enter.

Master Jupyter notebooks for Python coding, learning edit and command modes, creating and moving cells, markdown versus code, and essential shortcuts to run, save, and fix errors efficiently.

Learn technical analysis with python, using indicators and interactive plots in plotly and cufflinks, including open-high-low-close and candlestick charts; load and handle financial data with pandas.

Set up a dedicated conda environment for technical analysis with cufflinks, plotly, and yfinance using Anaconda, Jupyter, and precise package versions to ensure compatibility.

Download stock data from Yahoo Finance using the we finance package, specifying a ticker and date range to obtain daily price and volume in a pandas DataFrame for algorithmic trading.

Import stock data from stocks.csv with pandas, fix a multi-index header and parse dates to a datetime index, then plot line charts of close prices for Apple, GE, and Microsoft.

Create offline interactive line charts with Plotly and cufflinks, loading GE, Microsoft, and Apple prices, and explore hover data, zoom, pan, and stock comparison on selected dates.

Learn to create interactive cufflinks and Plotly charts in Python using Jupyter notebooks or a shell, including offline mode, full file paths, and Windows raw strings.

Customize interactive Plotly line charts by adjusting fill, color scales, themes, titles, and axis labels, and explore spread plots for two stocks like GE and Apple.

Visualize open, high, low, close in candlestick charts, using bullish green and bearish red candles and shadows. Compare candlesticks to open-high-low bar charts and create them with Plotly and cufflinks.

Adjust bar size to daily, intraday, weekly, or monthly to fit your use case, and downsample open, high, low, and close data with Pandas resample to weekly or monthly candles.

Explore visualizing price and volume data with Cufflinks to create interactive candlestick charts and integrated volume plots, including bullish and bearish color cues.

Master technical indicators with a config chart, including simple moving averages with periods 20 and 100, Bollinger bands, MACD, and DMI, visualized via cufflinks for interactive analysis.

Learn how trend lines identify uptrends by connecting increasing lows and downtrends by connecting decreasing highs, and understand how significant breaks signal breakdowns or breakouts.

Explore support and resistance levels and how breaches or breakouts signal buy or sell decisions. See trendlines as support and resistance, plus round-number psychology and polarity changes at price reversals.

Compare technical analysis with fundamental analysis and relate it to the efficient market hypothesis. Review proper and improper use cases and introduce forex day trading as a practical example.

Compare technical analysis and fundamental analysis, highlighting price and volume data versus financial statements and intrinsic fair value concepts, and their pros and cons. It advocates using both methods pragmatically.

Explore how technical analysis forecasts prices from historical price and volume data, while the efficient market hypothesis argues prices reflect all information, and irrational behavior creates mispriced instruments and opportunities.

Explore practical uses of technical analysis, distinguish helpful versus less useful applications, and learn to backtest indicators, manage risk, and gain a small edge in forex, CFDs, and futures.

Explore how forex trading works by understanding currency pairs like euro/usd, bid and ask prices, spreads and pips, base and quote currencies, and how to take long or short positions.

Explore simple moving averages and crossover strategies, build a backtester framework, measure performance against benchmarks with transaction costs, and apply object oriented programming to test multiple indicators.

Retrieve daily euro US dollar price data from a CSV, import with pandas, set a date time index, compute log returns, and prep for backtest of buy and hold strategy.

Examine a simple buy and hold strategy for euro/usd as a passive long, held 16 years, with a cumulative log return of -11% and an end value of about $0.89.

Analyze a buy-and-hold strategy to compute absolute performance, mean and risk metrics, including annualized mean return and maximum drawdown, illustrating tail risk via a drawdown of 44 percentage points.

Explore simple moving averages with short-term and long-term windows to generate buy and sell signals via SMA crossover. See how crossovers indicate trends and prepare for coding in Python.

Define and visualize an sma crossover strategy with 50 and 200 day moving averages on euro us dollar data using python and pandas, generating buy/short signals and a position plot.

Backtest a SMA 50 and SMA 200 crossover on euro US dollar using vectorized pandas code, compute strategy returns from buy and hold, and compare performance to buy and hold.

Identify the optimal short and long sma crossover within ranges using a run strategy and brute force. Compare performance to buy and hold and note backtesting and forward testing limits.

Develop and test multiple strategies with the sma backtester class, loading forex data, optimizing short and long parameters, and comparing forward tests to buy-and-hold across instruments.

Create a backtester class with an __init__ method to assign symbol, short window, long window, start, end, and a None results attribute, then instantiate and inspect its attributes.

Implement Getdata method to import price data from csv or sources, select an instrument, compute log returns and SMA indicators (SMA 50, SMA 200), and store the result in self.data.

Learn to implement a set_parameters method in a backtester to update short and long windows (e.g., 25 and 150) and refresh the data frame, illustrating encapsulation and protected attributes.

Explore the backtester’s test_strategy method, which processes data, computes long/short positions from SMA crosses, and measures absolute performance and outperformance versus buy and hold.

Learn to test strategies and compare them to buy and hold using the backtester plot_results method, which visualizes absolute performance and outperformance.

Create the update and run helper to set short and long SMA, then run tests, returning the absolute performance for brute force optimization of the strategy.

Add the optimized parameters method to the Smartpak tester to brute-force short and long ranges, returning the optimal parameters and the absolute performance.

Enhance usability by adding concise docstrings and a meaningful string representation for the Backtester class in vectorized backtesting, including ticker symbol, sma parameters, dates, and key methods.

Explore how bid-ask spreads and trading costs affect backtests in algorithmic trading, using euro/usd trades and 21 position changes. Learn about half-spread costs and zero commissions impact profitability.

Explore how to deduct trading costs in backtesting using proportional costs based on bid-ask spreads, applying to euro/usd, and compare net-of-cost strategy returns versus before costs.

Add a trading cost parameter to the backtester, apply proportional costs for euro US dollar, and show how costs shift the optimal strategy while noting backtesting without costs is useless.

Examine the price/sma crossover as a special case of sma crossover, using a backtester to test psma1 with window one and show results identical to the price, including trading costs.

Explore exponential moving averages and the sma comparison, and learn to code backtests and optimize crossover strategies. Build an ema backtest class and apply sma adjustments to reinforce skills.

Learn how exponential moving averages generate crossover buy and sell signals via short and long EMAs. Compare EMA with SMA and see practical plotting of EMA 20 and EMA 100.

Import pandas, numpy, and matplotlib; load the euro us dollar daily price data from 2004 to mid-2020 and compare strategy effectiveness with smart strategies.

Learn to calculate exponential moving averages with Python and pandas, compare EMA to SMA, and explore parameters like span, com, half-life, and alpha for a 50-period window.

Define an ema crossover backtest using short (50) and long (200) ema, where crossovers trigger long or short signals, similar to an sma crossover.

Explore vectorized backtesting of an EMA crossover strategy, calculating log returns, applying one-day position shifts, and accounting for proportional trading costs to compare the strategy with buy-and-hold performance.

Build an EMA backtesting class for vectorized trading strategies, extending the Backtester with short and long EMA parameters, transaction costs, and the optimized parameters method for EMAs.

Demonstrates backtesting an EMA crossover strategy with the Backtester EMA class, including parameter optimization and trading costs. Compares performance to buy-and-hold across Tesla and the Australian dollar euro pair.

Explore the ema crossover coding exercise by building a trading strategy that uses sma and ema crossovers. Develop systematic, precise coding to implement this strategy on your own.

Configure SMA 50 and EMA 50 crossover strategies for 2018–2019 and visualize how an EMA crossing above the SMA signals a long, while crossing below signals a short.

Create the Backtester class in the Jupyter notebook, systematically updating it to use SMA and EMA instead of EMA short/long, with configurable moving windows and distinct parameter optimization.

Explore the SMA EMA backtester class for vectorized backtesting of moving average crossover strategies, loading data from forex pairs csv, computing SMA and EMA, and testing parameters with forward testing.

Explore the moving average convergence divergence (mACD) oscillator, code backtests to optimize the strategies, and review in-sample and out-of-sample testing while creating the mACD backtest class.

Explore how the MACD oscillator uses EMA 12 and EMA 26 to form the blue MACD line and its 9-period EMA signal line to signal buy and sell.

Backtest an mACD crossover strategy on daily euro US dollar data from 2004 to 2020, and compare its performance with SMA and EMA crossovers using pandas, numpy, matplotlib, and seaborn.

Define a macd crossover trading strategy: go long when the macd crosses above the macd signal and short when it crosses below, using a position and a secondary axis visualization.

Vectorized backtesting evaluates a MACD crossover strategy on the euro US dollar, computing daily log returns, applying one-day-shifted positions, and accounting for trading costs to compare with buy and hold.

Learn to implement the MACD backtester, run backtests, compare with buy-and-hold, and visualize results across instruments. Understand how to optimize parameters, perform forward testing, and assess trading costs.

Build a macd backtester class for vectorized backtesting, incorporating short and long ema, and a signal moving window; optimize parameters with brute force and apply macd vs signal tests.

Explore alternative macd strategies and interpretations, including zero crossovers, macd divergences, and convergence with price, to refine buy and sell signals using ema crossovers.

Explore the relative strength index as a powerful oscillator and test RSI-based strategies with backtesting, optimization, performance measurement, risk management, and building the RSI backtest class.

Understand how the relative strength index signals reversals by marking overbought and oversold conditions, using a 20-period RSI with 70/30 bands.

Import pandas, numpy, and matplotlib to fetch euro/US dollar currency-pair data from 2004 to 2020, laying the groundwork for an RSI strategy in the next lecture.

Define U and D movement columns and compute 20-day moving averages. RSI equals average up over total movement, scaled 0–100; oversold below 30 suggests long, overbought above 70 suggests short.

Compute the RSI, interpret above 70 as overbought and below 30 as oversold, and translate these signals into short, long, or neutral positions with plotted thresholds.

Vectorized backtesting uses log returns, shifted positions, and proportional trading costs to compare an RSI strategy against buy-and-hold over 16 years, showing outperformance with low drawdown.

Watch the RSI backtester analyze forex data, optimize moving average periods and RSI bands, account for trading costs, and compare performance and risk metrics.

Build RSI backtester class with periods, upper and lower; compute RSI and moving averages; apply the strategy: RSI above upper goes short, below lower goes long, else neutral; optimize parameters.

Explore alternative RSI strategies and interpretations, including RSI calculations with U and D and simple or exponential moving averages, and identify bullish and bearish divergences and convergence as trading signals.

Learn why and how to combine two or more indicators, code and backtest strategies, and use examples like MACD and RSI for stronger trading signals.

Explore combining mACD and RSI to analyze price trends, plot both indicators on a single chart, backtest the 2019–20 strategy, and identify trading opportunities amid contradicting signals.

optimize and backtest the macd and rsi strategies separately for eurusd, comparing performance and preparing to combine them in the next lecture.

Combine MACD and RSI signals, align positions, and backtest the integrated strategy to compare with buy and hold, revealing a distinct risk-return profile from multi-indicator approaches.