Tag: algorithmictrading
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Biotech Genmab Hold Alert via Fibonacci Retracement Trading Simulations
SeekingAlpha: A ‘Strong Buy’ Call on Genmab. While analysts were issuing bearish calls on Danish biotech Genmab (GMAB), Seeking Alpha contributor Biologics had different thoughts. In fact, Genmab has risen about 6% for 2022 even as the broader biotech sector has shed about 27% for the year. Let’s examine the Genmab stock in terms of […]
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XOM SMA-EMA-RSI Golden Crosses ’22
Featured Photo by Johannes Plenio on Pexels. Today we will discuss the XOM stock using most basic technical trading indicators (TTIs) within the Python library ta-lib. Recall that this library is widely used by algo traders requiring to perform technical analysis of financial market data. It includes 150+ indicators such as ADX, MACD, RSI, Stochastic, […]
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The $ASML Trading Strategies via the Plotly Stock Market Dashboard
Featured Photo by Jeremy Waterhouse. Recently, Dr. Dividend shared his insights into $ASML EUV business. This post is a follow-up based upon the highly interactive Plotly Stock Market Dashboard. Let’s import/install the key libraries !pip install pandas_datareader Successfully installed pandas_datareader-0.10.0 !pip install ta Successfully installed ta-0.10.2 import numpy as npimport pandas as pdfrom pandas_datareader import […]
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A TradeSanta’s Quick Guide to Best Swing Trading Indicators
This post was motivated by the recent TradeSanta’s insights into Top 6 Indicators For Swing Trading. Key Takeaways: RSI/STOCH – early spot an opportunity EOM – predicts a current trend with confidence MACD – generate robust BUY/SELL signal alerts BB – double check MACD trading signals/alerts VO – simple market sentiment check Use automated trading software (e.g. bots)
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Risk/Return POA – Dr. Dividend’s Positions
Based upon the Portfolio Optimization Algorithm (POA) discussed earlier and the relevant POA QC analysis and comparisons, let’s look at the current stock positions suggested by Dr. Dividend (DD). Let’s define the following POA parameters: benchmark_ = [“^GSPC”,]portfolio_ = [‘AAPL’, ‘GOOG’, ‘COST’, ‘SBUX’, ‘DE’,’SOFI’,’APD’,’UNH’,’SHW’,’NVDA’] start_date_ = “2021-01-01”end_date_ = “2022-10-05”number_of_scenarios = 10000 trade_days_per_year = 252 delta_risk […]
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Algorithmic Testing Stock Portfolios to Optimize the Risk/Reward Ratio
Investors can optimize their stock portfolio by invoking backtesting within the realm of algorithmic trading. The goal is to optimize the specific portfolio by maximizing returns and the Sharpe ratio.
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S&P 500 Algorithmic Trading with FBProphet
We use Facebook’s Prophet to forecast S&P 500 stock adjusted close price. We plot the results simulating an initial investment of $1,000.