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Jefferey Neuman's Strategy

  • trendline - trendline breakout after a consolidation
  • breakout
  • candle
  • catalyst: bulk block deals / short selling / news / events / new products / results.
    • How to build a catalyst system that will give a number or convert non-deterministic data from the market and news to give a deterministic data?

Funnel

  1. Select stocks
  • Consider PE and PB and EPS ratios while selecting momentum stock
  1. Check Daily-Weekly and 4 hour Support and resistance

Tikam's Experimental System

the technicals that I use the most are:

  • For Trend: 50 and 200 day moving averages

  • For Momentum: Relative Strength Index

  • For Swings: MACD

  • Mean reversion

  • Hurst Exponent

  • Random Walk

  • Monte Carlo Distribution

  • Normal Distribution

  • Markov chain analysis and prediction

  • Moving Average Optimization

  • Proxy Investing - Invest in shovels which is made for digging gold.

  • RSI + SMA

  • trendline - trendline breakout after a consolidation

  • breakout

  • candle

  • catalyst: bulk block deals / short selling / news / events / new products / results

  • Proxy Investing - Invest in shovels which is made for digging gold

  • Mean reversion

  • Hurst Exponent

  • Random Walk

  • Monte Carlo Distribution

  • Normal Distribution

  • Markov chain analysis and prediction

  • Moving Average Optimization

Bivariate analysis

Bivariate analysis involves examining the relationship between two variables. In the context of stock price data, bivariate analysis can be used to examine the relationship between the stock price and one or more other variables (such as volume, volatility , macroeconomic or technical indicators) in order to forecast future stock prices.

  • the code builds a linear regression model to predict the stock price based on the volume and VIX variables, and uses the model to make predictions on the same data used to train the model. Finally, it evaluates the model using the mean squared error (MSE).

In-Depth Analysis:

Moving Averages

Quant Strategies

  1. Mean Reversion Trading:
  • Concept: This strategy assumes that asset prices will revert to their historical average or mean over time.
  • Python Implementation: Use statistical techniques such as Bollinger Bands or the Relative Strength Index (RSI) to identify overbought or oversold conditions.
  1. Trend Following:
  • Concept: This strategy relies on identifying and following the prevailing market trends.
  • Python Implementation: Utilize moving averages or trend indicators like the Moving Average Convergence Divergence (MACD) to detect trends and generate buy/sell signals.
  1. Pairs Trading:
  • Concept: This strategy involves trading two correlated assets simultaneously, taking advantage of temporary divergences in their prices.
  • Python Implementation: Analyze the historical price relationship between two assets and create trading signals based on deviations from their expected spread.
  1. Statistical Arbitrage:
  • Concept: Exploiting price inefficiencies in related financial instruments through statistical models.
  • Python Implementation: Develop a cointegration model or use machine learning techniques to identify mispricing and generate trading signals.
  1. Machine Learning-Based Strategies:
  • Concept: Use advanced machine learning algorithms to analyze market data and make trading decisions.
  • Python Implementation: Implement machine learning models such as decision trees, random forests, or neural networks for predicting price movements.
  1. Volatility Trading:
  • Concept: Exploit changes in market volatility to make trading decisions.
  • Python Implementation: Calculate historical volatility, use options strategies like straddle or strangle, or implement the Volatility Index (VIX) as a trading signal.
  1. Momentum Trading:
  • Concept: Capitalize on the continuation of existing trends by entering trades in the direction of the prevailing momentum.
  • Python Implementation: Use momentum indicators like the Relative Strength Index (RSI) or rate of change (ROC) to identify strong trends and generate buy/sell signals.
  1. Event-Driven Strategies:
  • Concept: Trade based on specific events, such as earnings announcements or economic releases.
  • Python Implementation: Develop algorithms that react to predefined events, leveraging sentiment analysis or natural language processing to assess news and social media sentiment.
  1. Market Making:
  • Concept: Act as a liquidity provider by continuously quoting buy and sell prices, profiting from the bid-ask spread.
  • Python Implementation: Implement algorithms that adjust bid and ask prices based on market conditions, ensuring a profit margin from the spread.
  1. Risk Parity:
  • Concept: Allocate capital based on the risk contribution of each asset in the portfolio, aiming for a balanced risk exposure.
  • Python Implementation: Utilize optimization techniques to allocate capital proportionally to assets, considering their historical volatility and correlation.
  1. Alpha Generation:
  • Concept: Alpha generation: these strategies aim to identify and exploit trading opportunities that can generate positive returns above a benchmark or market index by identifying mispricings in the market using algorithms.

References:

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Github quant and python tools

Try on 29 jan