# Innovative Multi-Stochastic Trading Techniques for Success

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## Chapter 1: Understanding the Stochastic Oscillator

The stochastic oscillator is widely appreciated for its straightforward application, simplicity, and compatibility with intricate trading strategies. In this article, we will delve into the oscillator and introduce a relatively obscure strategy that employs multiple confirmation signals derived from various stochastic oscillators.

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### The Stochastic Oscillator Explained

To grasp the stochastic oscillator, one must first understand normalization. This method confines values between 0 and 1 (or 0 and 100 if multiplied by 100). The process involves subtracting the minimum value over a specified lookback period from the current value, then dividing by the difference between the maximum and minimum values in that same period.

The stochastic oscillator identifies overbought and oversold conditions by integrating the highs and lows through the normalization formula. An overbought scenario indicates that the market is excessively bullish and likely to consolidate, while an oversold scenario suggests extreme bearishness, signaling a potential bounce. Thus, the stochastic oscillator acts as a contrarian indicator, aiming to highlight reactions during extreme market fluctuations.

Here's a function to calculate the stochastic oscillator based on OHLC data:

def stochastic(Data, lookback, high, low, close, where, genre='High-Low'):

# Adding a column

Data = adder(Data, 1)

if genre == 'High-Low':

for i in range(len(Data)):

try:

Data[i, where] = (Data[i, close] - min(Data[i - lookback + 1:i + 1, low])) / (max(Data[i - lookback + 1:i + 1, high]) - min(Data[i - lookback + 1:i + 1, low]))except ValueError:

pass

if genre == 'Normalization':

for i in range(len(Data)):

try:

Data[i, where] = (Data[i, close] - min(Data[i - lookback + 1:i + 1, close])) / (max(Data[i - lookback + 1:i + 1, close]) - min(Data[i - lookback + 1:i + 1, close]))except ValueError:

pass

Data[:, where] = Data[:, where] * 100

Data = jump(Data, lookback)

return Data

The following chart displays the EURUSD values alongside a 13-period stochastic oscillator.

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## Chapter 2: Crafting the Multi-Stochastic Strategy

The strategy revolves around several stochastic oscillators, each with different lookback periods, to obtain confirmation from multiple angles. For instance, if a 5-period stochastic reads 90 while a 100-period stochastic also shows 90, we can be more confident in considering a bearish position compared to relying on a single oscillator.

We can select multiple stochastic oscillators in two ways:

**Subjective Selection:**Choosing preferred lookback periods based on familiarity, such as the 5, 8, 14, and 20-period oscillators.**Regular Sequence:**Opting for a systematic approach, such as 10, 20, 30, etc. A personal favorite is to use Fibonacci numbers, which is merely a personal choice and should not be taken as a recommendation.

Regardless of the selection method, it's vital to back-test the periods and their combinations to identify the best fit. This optimization process takes time but is more scientific than arbitrary choices.

my_data = stochastic(my_data, 2, 1, 2, 3, 4)

my_data = stochastic(my_data, 3, 1, 2, 3, 5)

my_data = stochastic(my_data, 5, 1, 2, 3, 6)

my_data = stochastic(my_data, 8, 1, 2, 3, 7)

my_data = stochastic(my_data, 13, 1, 2, 3, 8)

my_data = stochastic(my_data, 21, 1, 2, 3, 9)

my_data = stochastic(my_data, 34, 1, 2, 3, 10)

my_data = stochastic(my_data, 55, 1, 2, 3, 11)

my_data = stochastic(my_data, 89, 1, 2, 3, 12)

my_data = stochastic(my_data, 144, 1, 2, 3, 13)

The following chart illustrates EURUSD hourly values alongside 10 stochastic oscillators with lookback periods of {2, 3, 5, 8, 13, 21, 34, 55, 89, 144}.

**Trading Rules:**

- A
**Buy**signal is triggered when all 10 oscillators are below 10. - A
**Sell**signal occurs when all 10 oscillators are above 90.

def signal(Data, buy, sell):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, 4:14].sum() < 50 and Data[i - 1, 4:14].sum() > 50 and Data[i - 2, 4:14].sum() > 50:

Data[i, buy] = 1elif Data[i, 4:14].sum() > 950 and Data[i - 1, 4:14].sum() < 950 and Data[i - 2, 4:14].sum() < 950:

Data[i, sell] = -1

return Data

To further refine trading rules, consider adjusting the oversold and overbought thresholds.

def signal(Data, buy, sell):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, 4:14].sum() < 50 and Data[i - 1, 4:14].sum() > 50 and Data[i - 2, 4:14].sum() > 50:

Data[i, buy] = 1elif Data[i, 4:14].sum() > 950 and Data[i - 1, 4:14].sum() < 950 and Data[i - 2, 4:14].sum() < 950:

Data[i, sell] = -1

return Data

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## Conclusion

Always conduct back-tests. It's crucial to maintain a skeptical outlook on others' strategies. What works for me may not suit you. I advocate for self-learning through practical experience rather than imitation. Understand the concepts and conditions of the strategy, then develop your own, testing and refining it before implementation.

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In summary, are the strategies I present realistic? Yes, but only when the environment is optimized (robust algorithms, low costs, trustworthy brokers, and effective risk management). These strategies aren't solely for trading; they aim to inspire innovative trading ideas, moving beyond outdated classical methods.

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## Chapter 3: Stochastic Trading Strategies in Action

The first video illustrates the Double Stochastic Forex Trading Strategy, demonstrating how to implement this approach effectively.

The second video covers the Triple Stochastic Trading Strategy, providing insights into enhancing trading performance using multiple oscillators.