{ "version": "https://jsonfeed.org/version/1", "title": "algotrading on CafeIO", "icon": "https://micro.blog/akarsh/avatar.jpg", "home_page_url": "https://akarsh.micro.blog/", "feed_url": "https://akarsh.micro.blog/feed.json", "items": [ { "id": "http://akarsh.micro.blog/2023/04/04/unlocking-the-secrets.html", "title": "Unlocking The Secrets of Algo Trading: Learn How to Use Python for Getting Data and Strategies…!!", "content_html": "
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With Python’s limitless libraries and tools, we can build a robust foundation to predict market trends, analyse financial data, and create advanced financial models.

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Hey there! In this post, I’m going to talk about setting things up for algo trading. In my previous video, I covered many aspects of algo trading, including technology, tools, markets, and data. I will build on top of that in this blog post. I would like to point out that this post is accompanied by a video which I shared before.

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You can watch the supplementary video and Follow Along the Video.

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Hardware & Software Setup

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You don’t need a fancy computer for this, anything online or a simple computer will work.

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Here’s what we will focus on

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We will be setting up a python environment, getting market data using Yahoo Finance, and doing some basic statistical analysis. We will then create a simple strategy and compare returns.

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  1. Get market data from Yahoo Finance.
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  2. Play with the data, get data from NSE, and do stats on it.
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  3. Calculate moving averages and do plotting.
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  4. Try to do something known as Bollinger bands, which is a technical indicator.
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  5. Make a simple moving average-based strategy.
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More Details

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After installing the libraries, we will use Yahoo Finance to get market data. We will define a variable ticker and set start and end dates. Then, we will download the data and create a data frame. We will also reduce the number of days to make it easier to plot.

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Next, we will plot the high, lows, and search for patterns. We will also calculate some basic formulas like simple moving average, exponential moving averages, and Bollinger bands. Bollinger bands is a plus-minus two standard deviations up and down from a simple moving average.

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Finally, we will make a simple moving average-based strategy. The strategy will be to sell if a condition is met and hold if it is not.

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The Complete Code is shared below

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  1. Install necessary libraries.
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  2. Define variable ticker and set start and end dates.
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  3. Download data and create a data frame.
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  4. Reduce the number of days to make it easier to plot.
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  5. Plot high lows and search for patterns.
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  6. Calculate basic formulas like simple moving average, exponential moving averages, and Bollinger bands.
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Working with a Strategy

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In the world of algorithmic trading, having a well-defined strategy is crucial for success. A trading strategy is simply a set of rules that traders follow to generate signals that indicate when to buy, sell, or do nothing.

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One popular trading strategy is using the simple moving average (SMA). The SMA is a technical indicator that helps traders identify trends in the market. It calculates the average price of a security over a specific time period, such as 12 days, by adding up the closing prices of each day and dividing it by the number of days in the period.

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When using the SMA, traders typically buy a stock when its closing price is higher than its simple moving average and sell it when the closing price is lower. This approach can help traders identify potential uptrends or downtrends in the market and make informed decisions on when to enter or exit a trade.

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However, it’s important to note that no trading strategy is foolproof. Market conditions can change rapidly, and unforeseen events can impact a stock’s performance. Therefore, it’s essential to continually monitor the market and adjust trading strategies as needed.

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In addition to the SMA, there are many other technical indicators and trading strategies that traders can use to make informed decisions in the market. Some popular ones include the relative strength index (RSI), the moving average convergence divergence (MACD), and the Bollinger Bands.

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When studying financial engineering and data science, it’s essential to clean up data to ensure that the calculations are accurate. This often involves dropping unnecessary columns and removing any errors or outliers that may skew the results.

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In summary, having a well-defined trading strategy is essential for success in algorithmic trading. The SMA is just one of many technical indicators and trading strategies that traders can use to make informed decisions in the market. However, it’s important to continually monitor the market and adjust strategies as needed to stay ahead of the game.

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Complete Code

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#pip install yfinance plotly cufflinks\n\n# Core Imports\nimport pandas as pd\nimport numpy as np\nimport datetime\nimport yfinance as yf\nimport matplotlib.pyplot as plt\n\n#Ticker is the stock that we want to get\nTicker = "^NSEI"\n\n\n# Get Sample data based on a Ticker\nend1 = datetime.date.today()\nstart1 = end1 - pd.Timedelta(days=5)\n\ndf = yf.download(Ticker, start=start1, end=end1, interval="5m" )\nprint(df.head())\ndf.info()\n\n# Plotting the Data\n\ndf1a = df.copy()\ndf1a.loc['2023-02-13', ['Open', 'High', 'Low', 'Close']].plot(grid=True, linewidth=1, figsize=(14, 9))\n\n# Just the Closing Price\ndf1a['Close'].plot(grid=True, linewidth=1, figsize=(14, 9))\n\n# Core Calculation Functions \n# SMA\ndef get_sma(prices, rate):\n  return prices.rolling(rate).mean()\n\n# EMA\ndef get_ema(prices, rate):\n  return prices.ewm(span=ema, adjust=False).mean()\n\n# Bollinger Bands\n\n# Bollinger bands are a technical analysis tool used by traders to identify potential entry and exit points in the market. They are created by plotting a moving average of the price along with two standard deviation lines above and below it. By doing so, Bollinger bands can provide an indication of whether a stock is overbought or oversold. They also help traders identify potential breakouts or reversals in the market. With this knowledge, traders can make more informed decisions when entering or exiting a position in the market.\n\ndef get_bollinger_bands(prices,rate):\n    sma = get_sma(prices, rate)\n    std = prices.rolling(rate).std()\n    bollinger_up = sma + std * 2 # Calculate top band\n    bollinger_down = sma - std * 2 # Calculate bottom band\n    return bollinger_up, bollinger_down\n\n\n# Other Functions\n\ndef download_daily_data(ticker, start, end):\n    """ \n    The function downloads daily market data to a pandas DataFrame \n    using the 'yfinance' API between the dates specified.\n    """\n    data = yf.download(ticker, start, end)\n    \n    return data\n\ndef compute_daily_returns(data):\n    """ \n    The function computes daily log returns based on the Close prices in the pandas DataFrame\n    and stores it in a column  called 'cc_returns'.\n    """\n    data['cc_returns'] = np.log(data['Close'] / data['Close'].shift(1))\n    return data\n\n# Generate Bollinger bands for the above\n\nbollinger_up, bollinger_down = get_bollinger_bands(df1a['Close'],20)\n\n\n# Plot the Results\n\nsymbol = 'NSE'\nclosing_prices = df1a['Close']\n\nplt.title(symbol + ' Bollinger Bands')\nplt.xlabel('Days')\nplt.ylabel('Closing Prices')\nplt.plot(closing_prices, label='Closing Prices')\nplt.plot(bollinger_up, label='Bollinger Up', c='g')\nplt.plot(bollinger_down, label='Bollinger Down', c='r')\nplt.legend()\nplt.show()\n\n\n# Strategy Perform Calculations\n\n# Remove the Un-necessary Columns\ndf1a.drop(columns=['High', 'Low', 'Volume'], inplace=True)\n\n# Create a new colum which captures the percentage cahnge from previous day\ndf1a['cc_returns'] = df1a['Close'].pct_change()\n\n# Define a short 12 day sma \nsma = 12\ndf1a['sma'] = df1a['Close'].rolling(window=sma).mean()\n\nprint(df1a.head())\nprint(df1a.tail())\n\n# If the closging price is higher that sma buy else do nothing\ndf1a['position'] = np.where((df1a['Close'] > df1a['sma']), 1, 0)\ndf1a['position'] = df1a['position'].shift(1)\ndf1a['position'].value_counts()\n\n# Plotting the above strategy returns\n\ndf1a['strategy_returns'] = df1a['cc_returns'] * df1a['position']\n\n\ndf1a['strategy_returns'] = 1 + df1a['strategy_returns']\ndf1a['cc_returns'] = 1 + df1a['cc_returns']\n\nprint(df1a.head())\nprint(df1a.tail())\n\ndf1a[['cc_returns', 'strategy_returns']].cumprod().plot(grid=True, figsize=(9, 5))\n\nprint('Buy and hold returns: ', np.round(df1a['cc_returns'].cumprod()[-1], 2))\nprint('Strategy returns: ', np.round(df1a['strategy_returns'].cumprod()[-1], 2))\nHyperWrite Logo\n
\n", "date_published": "2023-04-04T09:00:00+05:30", "url": "https://akarsh.micro.blog/2023/04/04/unlocking-the-secrets.html", "tags": ["algotrading","Python","Quants","Financial Engineering"] }, { "id": "http://akarsh.micro.blog/2023/03/28/why-is-it.html", "title": "Why is it Important to understand TimeSeries in Algo Trading?", "content_html": "
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Time series analysis plays an important role in understanding and forecasting stock market behaviour

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What is Algorithmic Trading?

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Algorithmic trading, also known as algo trading, is a method of executing trades in financial markets using pre-programmed instructions that automatically execute trades based on certain criteria or market conditions.

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Algo trading is commonly used by institutional investors such as hedge funds, banks, and pension funds, as well as by individual traders. The use of algorithms can lead to increased efficiency and speed of trading, as well as reduced transaction costs. However, algo trading can also come with risks, such as the potential for errors in the programming or unexpected market movements that could trigger unintended trades.

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What is Time Series?

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In the context of the stock market, a time series is a set of historical stock market data that is collected and organised over a period of time. This data typically includes the prices of a particular stock, index, or other financial instrument at different points in time, and can also include other metrics such as trading volume or market capitalisation.

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If you’re interested in trading, then you’ve likely heard of algorithmic trading, or algo trading for short. This approach uses sophisticated mathematical models and algorithms to analyze market data and identify buying and selling opportunities automatically. By doing so, algo trading eliminates the need for human intervention in executing trades.

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When it comes to analyzing stock market data, one popular method is time series analysis. This technique involves studying historical data to identify patterns, trends, and relationships between different variables.

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Technical analysts often use time series data to develop trading strategies and make predictions about future market movements. By using tools such as moving averages, chart patterns, and technical indicators, traders can identify patterns and trends in the data and make informed decisions about buying, selling, or holding stocks or other financial instruments.

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By leveraging the power of algo trading and time series analysis, traders can gain a more profound understanding of the stock market and make more informed investment decisions.

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Components of Stock Market Data

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Stock market data typically includes a variety of components that can provide insights into the performance of individual stocks, as well as broader market trends. Some of the most common components of stock market data include:

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  1. Stock prices: The prices of individual stocks at different points in time are a key component of stock market data. This includes the opening and closing prices, as well as intra-day highs and lows.
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  2. Volume: The total number of shares traded for a particular stock over a given time period is known as volume. Volume can be an important indicator of market sentiment and can help investors understand the level of interest in a particular stock.
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  3. Market capitalisation: Market capitalisation is the total value of a company’s outstanding shares. It is calculated by multiplying the total number of shares by the current market price per share.
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  4. Dividends: Dividends are payments made by companies to their shareholders as a share of profits. Dividend data can provide insights into a company’s financial health and growth potential.
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  5. Earnings: Earnings reports provide information on a company’s financial performance, including revenues, expenses, and net income. This information can be used to evaluate the financial health and growth potential of a company.
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  6. News and events: News and events that impact the stock market, such as company announcements, economic reports, and geopolitical events, can also be an important component of stock market data.
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By analyzing these components and searching for patterns and trends, investors and traders can gain a more profound understanding of the stock market and make more informed investment decisions.

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If you play close attention, most of these components are time—varying. Typically, Period and Interval are two aspects of time which are factored in. For example,

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Components of Time Series

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By identifying these components in a time series, analysts can gain a more in-depth understanding of the underlying patterns and trends in the data.

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This information can be used to make predictions, develop forecasting models, and inform decision-making for Quantitative finance.

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  1. Trend: A trend is a long-term increase or decrease in the data over time. It can be upward, downward, or stable.
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  2. Seasonality: Seasonality refers to the regular and periodic fluctuations in the data that occur within a year or other fixed time period. For example, sales of winter clothing may be higher during the winter months, while sales of summer clothing may be higher in the summer months.
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  3. Cyclical components: Cyclical components are fluctuations in the data that occur over a period longer than a year. They can be influenced by factors such as economic cycles or business cycles.
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  4. Irregular components: Irregular components are unexpected fluctuations in the data that are not accounted for by trend, seasonality, or cyclical components. They may be due to random events, measurement error, or other factors.
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Putting Theory to Practise

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We will look at two simple strategies that are purely time series based and are extremely popular in the trading world. I would like to point out that this is not a trading advice and the examples just illustrate the concept.

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Golden Crossover strategy

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The Golden Crossover Strategy is a popular technical analysis trading strategy used in the stock market. It involves using two moving averages of different lengths to identify buying and selling signals.

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The Golden Crossover occurs when the 50-day moving average crosses above the 200-day moving average, indicating a bullish signal. This is interpreted as a buy signal, as it suggests that the stock is trending upwards and has the potential to increase in value.

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import yfinance as yf\nimport numpy as np\n\n# Download historical stock data for a given ticker symbol\nstock_data = yf.download('AAPL', start='2020-01-01', end='2022-01-01')\n\n# Calculate the 50-day and 200-day moving averages\nstock_data['50_day_ma'] = stock_data['Adj Close'].rolling(window=50).mean()\nstock_data['200_day_ma'] = stock_data['Adj Close'].rolling(window=200).mean()\n\n# Generate signals based on the Golden Crossover strategy\nstock_data['signal'] = 0.0\nstock_data['signal'][50:] = np.where(stock_data['50_day_ma'][50:] \n                                      > stock_data['200_day_ma'][50:], 1.0, 0.0)\n\n# Calculate the positions based on the signals\nstock_data['position'] = stock_data['signal'].diff()\n\n# Plot the stock prices, moving averages, and trading signals\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(10,5))\nplt.plot(stock_data['Adj Close'], label='AAPL')\nplt.plot(stock_data['50_day_ma'], label='50-day MA')\nplt.plot(stock_data['200_day_ma'], label='200-day MA')\n\n# Plot the buy and sell signals\nplt.plot(stock_data[stock_data['position'] == 1].index, \n         stock_data['50_day_ma'][stock_data['position'] == 1], \n         '^', markersize=10, color='green', label='buy')\nplt.plot(stock_data[stock_data['position'] == -1].index, \n         stock_data['50_day_ma'][stock_data['position'] == -1], \n         'v', markersize=10, color='red', label='sell')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.title('Golden Crossover Strategy for AAPL')\nplt.legend()\nplt.show()\n
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ARIMA Model (Autoregressive Integrated Moving Average)

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The ARIMA model consists of three components: autoregression, integration, and moving average. Autoregression refers to the use of past values of the time series as predictors for future values. Integration involves transforming the data to make it stationary, meaning that the mean and variance are constant over time. Moving average involves using past errors as predictors for future values.

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To use ARIMA for algo trading, historical stock price data is first analyzed to identify the trend, seasonality, and other components of the time series. The data is then transformed to make it stationary, and an ARIMA model is fit to the transformed data. The model can be used to forecast future stock prices, and these forecasts can be used to make trading decisions automatically.

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Summary

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Algorithmic trading is the use of pre-programmed instructions to automatically execute trades in financial markets based on certain criteria or market conditions. It is popular among institutional investors and individual traders for its speed and efficiency, although it also comes with risks. Time series, on the other hand, refers to a set of historical stock market data collected and organised over a period of time, typically including stock prices and trading volumes. Technical analysts use time series analysis to identify patterns and trends and develop trading strategies.

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Components of stock market data include stock prices, volume, market capitalisation, dividends, earnings, and news and events, all of which can provide insights into the performance of individual stocks and broader market trends. In analyzing these components and searching for patterns and trends, investors and traders can gain a more profound understanding of the stock market and make more informed investment decisions. Time series analysis helps analysts identify the components of a time series, such as trend, seasonality, cyclical components, and irregular components, which can inform decision-making in quantitative finance.

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The article also includes examples of two simple trading strategies based on time series analysis: the Golden Crossover Strategy and the Momentum Strategy. These strategies are not intended as trading advice but are used to illustrate the concept of time series analysis in trading.

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References

\n\n", "date_published": "2023-03-28T09:00:00+05:30", "url": "https://akarsh.micro.blog/2023/03/28/why-is-it.html", "tags": ["algotrading","Machine Learning"] }, { "id": "http://akarsh.micro.blog/2023/03/19/setting-things-up.html", "title": "Setting things Up", "content_html": "

Algorithmic trading, also known as algo trading, is the use of computer programs to follow a defined set of instructions for placing trades. It is a form of automated trading that allows traders to execute orders with speed and precision. Algo trading helps traders to identify and react to market opportunities faster, reduces emotions, and increases discipline. It also allows traders to track multiple accounts and multiple markets simultaneously.

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In this post/video we will get started with foundational code in python and cover the following introductory material.

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• Setting up yfinance library

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• Basic Operations and Plotting

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• SMA, EMA, Bollinger Bands

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• Simple Strategy based on SMA

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Python is a popular language for algorithmic trading. It has a wide range of libraries and tools for performing data analysis, backtesting and paper trading. It is also used for developing trading strategies and optimising them for different markets. Python has a wide range of libraries for performing data analysis, backtesting and paper trading. The most popular libraries include Pandas, Numpy, Scipy, Matplotlib, Scikit-learn, and Statsmodels.

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Code

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youtu.be/uvby-fWe8…

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Definitions

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Simple Moving Average : A simple moving average (SMA) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range.

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• www.investopedia.com/terms/s/s…

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Exponential Moving Average : An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.

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• www.investopedia.com/terms/e/e…

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Bollinger Band : A Bollinger Band® is a technical analysis tool defined by a set of trendlines. They are plotted as two standard deviations, both positively and negatively, away from a simple moving average (SMA) of a security’s price and can be adjusted to user preferences.

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• www.investopedia.com/terms/b/b…

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To get started with algorithmic trading, one needs to understand the different strategies and how to implement them. Strategies include mean reversion, momentum, arbitrage, market making, pair trading, and volatility trading. Mean reversion is the theory that suggests that prices tend to revert to their mean or average. Momentum is the theory that suggests that prices move in the same direction for a period of time. Arbitrage is the strategy of taking advantage of differences in prices of the same security in different markets. Market making is a strategy of providing liquidity to the market by buying and selling the same security. Pair trading is the strategy of buying and selling two correlated securities. Volatility trading is the strategy of trading on the volatility of the market.

\n", "date_published": "2023-03-19T15:00:00+05:30", "url": "https://akarsh.micro.blog/2023/03/19/setting-things-up.html", "tags": ["algotrading"] }, { "id": "http://akarsh.micro.blog/2023/03/17/introduction-to-algo.html", "title": "Introduction to Algo Trading", "content_html": "

Introduction

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Algorithmic trading, also known as algo trading, is the use of computer programs to follow a defined set of instructions for placing trades. It is a form of automated trading that allows traders to execute orders with speed and precision. Algo trading helps traders to identify and react to market opportunities faster, reduces emotions, and increases discipline. It also allows traders to track multiple accounts and multiple markets simultaneously.

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In this blog post, we will explore the basics of algo trading and its components. We’ll look at what a ticker is and the difference between quants and technical and fundamental analysis. We’ll also look at the basics of Python, strategies, libraries, backtesting, and paper trades.

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https://www.youtube.com/watch?v=uvby-fWe8Ts

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Ticker, Liquidity & Order Book

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Let’s start by looking at what a ticker is. A ticker is the state of an order book in any given instance. The tick includes the trades executed in the given instance with the bid/ask information. For each trading interval, four price points are available: open, high, low, and close. There are two kinds of participants: buyers and sellers. Participants place orders as bid and ask.

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Liquidity can be defined as the flow of bid and ask orders in a given time interval. If the liquidity is high, it means it is easy to trade in the instrument and the slippage cost will be lower for the participants. The details of the bid and ask and trades at any given time are considered as an order book. The rule of the order book is, that the highest bid price in the market is the best bid and the lowest ask is the best ask. Most of the exchanges follow the price-time priority rule, where the bigger bid and smaller ask will be given precedence in trade execution. If the price is the same for the two orders then the time will factor will be used. The order that arrived earlier will be given priority in trade execution.

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Order Types

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There are different types of orders available, such as market orders, limit orders, stop orders, iceberg orders, fill or kill orders, and immediate or cancel orders.

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Quants, Technical & Fundamental Analysis

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Now let’s look at the difference between quants and technical and fundamental analysis.

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• Quants use mathematical and statistical models to analyze financial data and make predictions.

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• Technical analysis is the study of past price movements and trading volumes to identify patterns and make predictions about future price movements.

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• Fundamental analysis is the study of economic and financial factors that can affect the price of a security.

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Python is a programming language used by quants and traders to develop algo trading systems. It has libraries for data analysis, machine learning, and quantitative analysis.

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Strategies are the core of algo trading.

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Backtesting is the process of testing a strategy on historical data to evaluate its performance. Paper trading is the practice of simulating the trading of securities without using actual money.

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Summary

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In conclusion, algo trading is a form of automated trading that allows traders to execute orders with speed and precision. It helps traders to identify and react to market opportunities faster, reduces emotions, and increases discipline.

\n", "date_published": "2023-03-17T13:52:27+05:30", "url": "https://akarsh.micro.blog/2023/03/17/introduction-to-algo.html", "tags": ["algotrading"] } ] }