Algorithmic strategy building and testing is a process that involves using mathematical models and programming techniques to develop, backtest, and optimize trading strategies. The process typically includes the following steps:

1. Identifying a trading idea: This is the first step in developing an algorithmic trading strategy. The idea may come from a variety of sources, such as market observations, historical data analysis, or fundamental analysis.
2. Formulating a hypothesis: Once a trading idea is identified, the next step is to formulate a hypothesis about how the market will behave. This hypothesis should be specific and testable.
3. Backtesting: After a hypothesis has been formulated, the next step is to backtest the strategy using historical data. Backtesting involves simulating the strategy on historical data to see how it would have performed in the past. This step is important to evaluate the effectiveness of the strategy and identify any potential issues.
4. Optimizing: After backtesting, the next step is to optimize the strategy to improve its performance. This may involve adjusting the parameters of the strategy, changing the underlying market data, or changing the logic of the algorithm.
5. Forward testing: Once the strategy has been optimized, it is important to forward test the strategy on out-of-sample data to ensure that it generalizes well. This step is essential in avoiding overfitting, a situation where the strategy performs well on historical data but not in real-time
6. Implementation and monitoring: Once the strategy has been developed, optimized, and forward-tested, it can be implemented in a live trading environment and monitored for performance.
7. Continuously improve: At this point, the trader or the investor should continuously monitor the performance and make adjustments as necessary to adapt to the changing market conditions.

Algorithmic trading can help traders and investors to quickly and systematically analyze large amounts of data, identify profitable trading opportunities, and execute trades automatically. However, it's important to keep in mind that the process of developing an algorithmic trading strategy is iterative and requires a significant amount of time and expertise in both programming and trading. And it's always advisable to use a robust risk management strategy when using these models.