Introduction to Psychological Levels

Psychological levels are pivotal in trading analysis. They receive more mental emphasis compared to other price levels. For instance, which of these prices would you more likely remember: 1.1500 on EURUSD or 1.3279 on GBPUSD? Clearly, round numbers stand out more in our minds. Another aspect of these levels is their significance—par values such as 1.000 on USDCHF or 100.00 on USDJPY are examples of key psychological levels. Traders often place their orders around these points, which can lead to notable market reactions.

Our objective is to devise an algorithm that triggers trades when the market hits a psychological level. This involves a straightforward loop function in Python, which we will delve into later.

I have also teamed up with Lumiwealth, a resource where you can learn to create various algorithms. They offer comprehensive courses covering topics from algorithmic trading to blockchain and machine learning, which I highly recommend.

Creating the Scanner

As with any thorough research method, the goal is to back-test indicators and assess their value as enhancements to our existing trading frameworks. By applying our algorithm to the hourly values of AUDUSD, we can generate signals as illustrated in the following chart:

Every time the market price closes at a psychological level rounded to four decimal places, the algorithm generates a signal. To determine if the signal is bullish or bearish, the algorithm compares the current price with the price from 20 periods prior. If the current price is lower than that of 20 periods ago, the signal is deemed bullish, suggesting the market is at support. Conversely, if the current price is higher, the signal is bearish, indicating resistance.

def psychological_levels_scanner(data, close, where):

# Adding buy and sell columns

data = adder(data, 10)

# Rounding for ease of use

data = rounding(data, 4)

# Threshold

level = 0

# Scanning for Psychological Levels

for i in range(len(data)):

for i in range(len(data)):

if data[i, close] == level:

data[i, where] = 1

level = round(level + 0.01, 2)

if level > 5:

break

return data

Notice that the signals generated are generally reliable, as they frequently occur at critical reversal points. While this alone may not guarantee profitability, it certainly adds value to our trading strategies.

It’s essential to focus on the underlying concepts rather than just the code. You can find the codes for most of my strategies in my published works. The critical aspect is to understand the techniques and strategies themselves.

For a more in-depth analysis of current market positions and future trends, consider my weekly market sentiment report, which utilizes both complex and straightforward models. You can find more details through this link.

Summary

To summarize, my aim is to enhance the field of objective technical analysis by promoting transparent techniques and strategies that undergo rigorous back-testing before implementation. This approach aims to combat the subjective reputation often associated with technical analysis.

Medium serves as a platform for engaging reads. I personally explored numerous articles before I began writing. If you're interested, consider joining Medium using my referral link at no extra cost to you.

I suggest following these steps whenever you encounter a trading technique or strategy:

1. Maintain a critical mindset and eliminate emotional bias.
2. Back-test the strategy using realistic simulations.
3. If promising results arise, optimize the strategy and conduct a forward test.
4. Always factor in transaction costs and potential slippage in your simulations.
5. Incorporate risk management and position sizing in your tests.

Ultimately, even after taking these precautions, it's crucial to remain vigilant and monitor your strategy, as market dynamics can shift, impacting profitability.