Predicting stock prices using Long Short-Term Memory (LSTM) networks has become a popular approach due to their ability to capture long-term dependencies in sequential data. LSTM is a type of recurrent neural network (RNN) that is well-suited for time series forecasting tasks like stock price prediction.

LSTM models analyze historical stock price data to learn patterns and trends, which they then use to make predictions about future price movements. These models typically take as input a sequence of past stock prices and use it to predict the next price in the sequence. By training on historical data, LSTM models can learn to identify features and patterns that are indicative of future price movements.

However, it's important to note that predicting stock prices is inherently challenging due to the complex and unpredictable nature of financial markets. While LSTM models can provide valuable insights and predictions, they are not foolproof and should be used as part of a broader investment strategy.

In terms of risk-reward strategies for making profit in the stock market, there are several approaches that investors commonly use:

1. Buy and Hold: This strategy involves purchasing stocks and holding onto them for an extended period, typically with the expectation that they will increase in value over time. While this strategy can be relatively low-risk, it requires patience and a long-term investment horizon.

2. Day Trading: Day traders buy and sell stocks within the same trading day, attempting to profit from short-term price fluctuations. This strategy can be high-risk, as it requires making quick decisions based on intraday price movements.

3. Value Investing: Value investors look for stocks that are trading below their intrinsic value, based on fundamental analysis of factors such as earnings, dividends, and growth potential. This strategy aims to buy undervalued stocks and hold them until their true value is recognized by the market.

4. Technical Analysis: Technical analysts study past price and volume data to identify patterns and trends that can be used to predict future price movements. This strategy relies on charts and technical indicators to make trading decisions.

5. Diversification: Diversifying your investment portfolio across different asset classes, industries, and geographic regions can help reduce risk and maximize returns over the long term.

In terms of developing a front end for stock market analysis and visualization, Tkinter is a popular choice for creating graphical user interfaces (GUIs) in Python. Tkinter provides a simple and intuitive way to design and implement GUIs for applications like stock market analysis tools.

Using Tkinter, you can create interactive dashboards and visualizations that allow users to explore stock market data, view predictions generated by LSTM models, and analyze risk-reward strategies. By combining the power of LSTM for stock price prediction with a user-friendly interface developed using Tkinter, you can create a powerful tool for investors to make informed decisions in the stock market.