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design a very advanced level market making algo in python use machine learning and risk management
First, we will start by importing necessary libraries such as pandas, numpy, scikit-learn, and tensorflow.

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import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import tensorflow as tf
Next, we will load the historical data of the asset we want to market make for and preprocess it using MinMaxScaler to scale the data between 0 and 1.

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# Load historical data
data = pd.read_csv('asset_data.csv')

# Scale data between 0 and 1
scaler = MinMaxScaler()
data = scaler.fit_transform(data)
We will then split the data into training and testing sets.

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# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data[:, :-1], data[:, -1], test_size=0.2)
Next, we will define the architecture of the neural network using tensorflow and train it on the training data.

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# Define neural network architecture
model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, input_shape=(X_train.shape[1],), activation='relu'),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1, activation='linear')
])

# Compile model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train model
model.fit(X_train, y_train, epochs=100)
We will then evaluate the model on the testing data and calculate the mean squared error to evaluate the performance of the model.

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# Evaluate model on testing data
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)
Finally, we will implement risk management by setting a maximum loss threshold and stop loss orders.

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# Set maximum loss threshold
max_loss = 0.05

# Set stop loss orders
stop_loss = 0.01
We will then use the trained model to make predictions and make market making decisions based on the predicted values and the risk management parameters set.

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# Get current asset price
current_price = get_current_price()

# Make prediction
predicted_price = model.predict(current_price)

# Make market making decision based on prediction and risk management parameters
if predicted_price > current_price + max_loss:
    # Place buy order
elif predicted_price < current_price - stop_loss:
    # Place sell order
else:
    # Do nothing
This is a basic example of a market making algorithm that uses machine learning and risk management. It can be further improved by adding more features, adjusting the architecture of the neural network, and fine-tun




Benjamin Robert
design an advanced level quick and short term market follower trading algo in python
First, we will start by importing necessary libraries such as pandas, numpy, and ta.

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import pandas as pd
import numpy as np
import ta
Next, we will load the historical data of the asset we want to trade and preprocess it using ta library to calculate various technical indicators.

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# Load historical data
data = pd.read_csv('asset_data.csv')

# Calculate technical indicators
data = ta.add_all_ta_features(data, open="Open", high="High", low="Low", close="Close", volume="Volume")
We will then define the features we want to use for our trading strategy and split the data into training and testing sets.

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# Define features
features = ['RSI', 'MACD', 'ADX']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data[features], data['Close'], test_size=0.2)
Next, we will define the trading strategy by setting the conditions for buying and selling based on the technical indicators.

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# Define trading strategy
def trade(data):
    buy = []
    sell = []
    flag = -1
    for i in range(len(data)):
        if data['RSI'][i] < 30 and data['MACD'][i] > 0 and data['ADX'][i] > 20:
            buy.append(i)
            if flag != 1:
                sell.append(i)
                flag = 1
        elif data['RSI'][i] > 70 or data['MACD'][i] < 0 or data['ADX'][i] < 20:
            sell.append(i)
            if flag != 0:
                buy.append(i)
                flag = 0
        else:
            if flag == 1:
                sell.append(i)
                flag = 0
            elif flag == 0:
                buy.append(i)
                flag = 1
    return buy, sell

# Get buy and sell signals
buy, sell = trade(data)
Finally, we will implement the trading algorithm by placing buy and sell orders based on the signals generated by the trading strategy.

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# Initialize variables
cash = 100000
shares = 0

# Place buy and sell orders
for i in range(len(buy)):
    if buy[i] > sell[i]:
        # Place buy order
        cash, shares = buy_order(cash, shares, data['Close'][buy[i]])
    elif sell[i] > buy[i]:
        # Place sell order
        cash, shares = sell_order(cash, shares, data['Close'][sell[i]])