diff --git a/.gitignore b/.gitignore
index 346901b..9054f90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,4 +81,6 @@ server/yarn.lock
 yarn.lock
 server/.yarn/
 
-packages/snap/snap.manifest.json
\ No newline at end of file
+packages/snap/snap.manifest.json
+
+__pycache__
\ No newline at end of file
diff --git a/forecaster/.dockerignore b/forecaster/.dockerignore
new file mode 100644
index 0000000..21abcf3
--- /dev/null
+++ b/forecaster/.dockerignore
@@ -0,0 +1,17 @@
+__pycache__/
+.git
+.vscode
+.dockerignore
+.gitignore
+.env
+config
+build
+node_modules
+docker-compose.dev.yaml
+docker-compose.prod.yaml
+docker-compose.yaml
+Dockerfile
+Dockerfile.dev
+Dockerfile.prod
+Makefile
+README.md
\ No newline at end of file
diff --git a/forecaster/Dockerfile b/forecaster/Dockerfile
new file mode 100644
index 0000000..0d3266d
--- /dev/null
+++ b/forecaster/Dockerfile
@@ -0,0 +1,22 @@
+# Use the official Python base image
+FROM pytorch/pytorch:latest
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file to the working directory
+COPY requirements.txt .
+
+RUN echo $(python3 -m site --user-base)
+
+# Install the Python dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code to the working directory
+COPY . .
+
+# Expose the port on which the application will run
+EXPOSE 8000
+
+# Run the FastAPI application using uvicorn server
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"]
\ No newline at end of file
diff --git a/forecaster/README.md b/forecaster/README.md
new file mode 100644
index 0000000..1b36883
--- /dev/null
+++ b/forecaster/README.md
@@ -0,0 +1,18 @@
+# Python APT to USD rate forecasting engine
+This is a simple python script that uses the BiLSTM model architecture to forecast the APT to USD rate for the next few minutes,given the data of last 16 minutes.
+
+## Start the server
+```bash
+cd bin/
+./deploy.sh up
+```
+This command might take more than 10 minutes on first run as it will download the docker images and build the containers.
+Server will run on localhost:9000
+View the documentation at localhost:9000/docs
+
+## Stop the server
+```bash
+cd bin/
+./deploy.sh down
+```
+
diff --git a/forecaster/apt.csv b/forecaster/apt.csv
new file mode 100644
index 0000000..76243a8
--- /dev/null
+++ b/forecaster/apt.csv
@@ -0,0 +1,532 @@
+Minutes,Price
+0,8.331683333
+1,8.333775
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diff --git a/forecaster/bin/deploy.sh b/forecaster/bin/deploy.sh
new file mode 100755
index 0000000..0900c26
--- /dev/null
+++ b/forecaster/bin/deploy.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+if [[ $1 = "up" || $1 = "down" ]]; then
+    cd ..
+    fileEnv="docker-compose.yaml"
+    downOrUp=$1
+    echo "Running docker-compose -f docker-compose.yaml ${downOrUp}"
+    docker-compose -f docker-compose.yaml $downOrUp
+else
+    echo "Usage: ./deploy.sh [up|down]"
+fi
\ No newline at end of file
diff --git a/forecaster/docker-compose.yaml b/forecaster/docker-compose.yaml
new file mode 100644
index 0000000..f6ab6fb
--- /dev/null
+++ b/forecaster/docker-compose.yaml
@@ -0,0 +1,13 @@
+version: '3.1'
+services:
+  app:
+    image: app-image
+    build:
+      context: ./
+      dockerfile: Dockerfile
+    container_name: app-c
+    volumes:
+      - .:/app
+    ports:
+      - 9000:8000
+    
\ No newline at end of file
diff --git a/forecaster/requirements.txt b/forecaster/requirements.txt
new file mode 100644
index 0000000..99e21ba
--- /dev/null
+++ b/forecaster/requirements.txt
@@ -0,0 +1,7 @@
+fastapi==0.104.1
+matplotlib==3.5.1
+numpy==1.22.4
+pandas==2.1.4
+pydantic==1.10.12
+scikit_learn==1.3.0
+uvicorn>=0.15.0,<0.16.0
diff --git a/forecaster/server.py b/forecaster/server.py
new file mode 100644
index 0000000..1ff269c
--- /dev/null
+++ b/forecaster/server.py
@@ -0,0 +1,241 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+from copy import deepcopy as dc
+from sklearn.preprocessing import MinMaxScaler
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+
+class PriceData(BaseModel):  # price data of last 16 time stamps
+    price_0: float
+    price_1: float
+    price_2: float
+    price_3: float
+    price_4: float
+    price_5: float
+    price_6: float
+    price_7: float
+    price_8: float
+    price_9: float
+    price_10: float
+    price_11: float
+    price_12: float
+    price_13: float
+    price_14: float
+    price_15: float
+
+
+class TimeSeriesDataset(Dataset):
+    def __init__(self, X, y):
+        self.X = X
+        self.y = y
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, i):
+        return self.X[i], self.y[i]
+
+
+class LSTM(nn.Module):
+    def __init__(self, input_size, hidden_size, hidden_size_2, num_stacked_layers):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.hidden_size_2 = hidden_size_2
+        self.num_stacked_layers = num_stacked_layers
+        self.lstm = nn.LSTM(
+            input_size, hidden_size, num_stacked_layers, batch_first=True
+        )
+        self.relu = nn.ReLU()
+        self.fc = nn.Linear(hidden_size_2, 1)
+        self.fc2 = nn.Linear(hidden_size, hidden_size_2)
+
+    def forward(self, x):
+        batch_size = x.size(0)
+        h0 = torch.zeros(self.num_stacked_layers, batch_size, self.hidden_size).to(
+            device
+        )
+        c0 = torch.zeros(self.num_stacked_layers, batch_size, self.hidden_size).to(
+            device
+        )
+        out, _ = self.lstm(x, (h0, c0))
+        out = self.relu(out[:, -1, :])
+        out = self.fc2(out)
+        out = self.relu(out)
+        out = self.fc(out)
+        return out
+
+
+# Create a bidirectional LSTM model class
+class BiLSTM(nn.Module):
+    def __init__(self, input_size, hidden_size, hidden_size_2, num_layers):
+        super(BiLSTM, self).__init__()
+        self.hidden_size = hidden_size
+        self.hidden_size_2 = hidden_size_2
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(
+            input_size, hidden_size, num_layers, batch_first=True, bidirectional=True
+        )
+        self.fc = nn.Linear(hidden_size * 2, 1)
+
+    def forward(self, x):
+        # h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
+        # c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
+
+        out, _ = self.lstm(x)
+        out = self.fc(out[:, -1, :])
+
+        return out
+
+
+def prepare_df_for_lstm(df, n_steps):
+    df = dc(df)
+
+    df.set_index("Minutes", inplace=True)
+
+    for i in range(1, n_steps + 1):
+        df[f"Price(t-{i})"] = df["Price"].shift(i)
+    df.dropna(inplace=True)
+    return df
+
+
+def train_one_epoch(model, epoch, train_loader, loss_function, optimizer):
+    model.train(True)
+    print(f"Epoch: {epoch + 1}")
+    running_loss = 0.0
+
+    for batch_index, batch in enumerate(train_loader):
+        x_batch, y_batch = batch[0].to(device), batch[1].to(device)
+        output = model(x_batch)
+        loss = loss_function(output, y_batch)
+        running_loss += loss
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if batch_index % 100 == 99:
+            avg_loss_batches = running_loss / 100
+            print("Batch: {0}, loss: {1:.3f}".format(batch_index + 1, avg_loss_batches))
+            running_loss = 0.0
+
+
+def validate_one_epoch(model, test_loader, loss_function):
+    model.train(False)
+    running_loss = 0.0
+    for batch_index, batch in enumerate(test_loader):
+        x_batch, y_batch = batch[0].to(device), batch[1].to(device)
+        with torch.no_grad():
+            output = model(x_batch)
+            loss = loss_function(output, y_batch)
+            running_loss += loss
+
+    avg_loss_batches = running_loss / len(test_loader)
+
+    print("Val loss: {0:.3f}".format(avg_loss_batches))
+    print("*********************************************")
+
+
+app = FastAPI()
+
+
+@app.get("/")
+async def root():
+    return {"message": "Hello World"}
+
+
+def train():
+    data = pd.read_csv("apt.csv")
+    data = data[["Minutes", "Price"]]
+    lookback = 16
+    shifted_df = prepare_df_for_lstm(data, lookback)
+    shifted_df_as_np = shifted_df.to_numpy()
+    scaler = MinMaxScaler(feature_range=(-1, 1))
+    shifted_df_as_np = scaler.fit_transform(shifted_df_as_np)
+    X = shifted_df_as_np[:, 1:]  # all the rows(first index), but first col onwards
+    y = shifted_df_as_np[:, 0]
+    X = dc(np.flip(X, axis=1))
+    split_index = int(len(X) * 0.95)
+    X_train = X[:split_index]
+    X_test = X[split_index:]
+    y_train = y[:split_index]
+    y_test = y[split_index:]
+    # it is required for pytorch LSTMs to have an extra dimention at the end
+    X_train = X_train.reshape((-1, lookback, 1))
+    X_test = X_test.reshape((-1, lookback, 1))
+    y_train = y_train.reshape((-1, 1))
+    y_test = y_test.reshape((-1, 1))
+    X_train = torch.tensor(X_train).float()
+    X_test = torch.tensor(X_test).float()
+    y_train = torch.tensor(y_train).float()
+    y_test = torch.tensor(y_test).float()
+    train_dataset = TimeSeriesDataset(X_train, y_train)
+    test_dataset = TimeSeriesDataset(X_test, y_test)
+    batch_size = 9
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+    for _, batch in enumerate(train_loader):
+        x_batch, y_batch = batch[0].to(device), batch[1].to(device)
+        print(x_batch.shape, y_batch.shape)
+        break
+    model = BiLSTM(1, 8, 4, 1)
+    model.to(device)
+    learning_rate = 0.001
+    num_epochs = 100
+    loss_function = nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+    for epoch in range(num_epochs):
+        train_one_epoch(model, epoch, train_loader, loss_function, optimizer)
+        validate_one_epoch(model, test_loader, loss_function)
+    return model
+
+
+model = train()
+
+
+@app.post("/predict/")
+async def predict_price(price_data: PriceData):
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    model.eval()
+    price_data = np.array(
+        [
+            price_data.price_0,
+            price_data.price_1,
+            price_data.price_2,
+            price_data.price_3,
+            price_data.price_4,
+            price_data.price_5,
+            price_data.price_6,
+            price_data.price_7,
+            price_data.price_8,
+            price_data.price_9,
+            price_data.price_10,
+            price_data.price_11,
+            price_data.price_12,
+            price_data.price_13,
+            price_data.price_14,
+            price_data.price_15,
+        ]
+    )
+    price_data = torch.tensor(price_data).float()
+    # normalize the data
+    scaler = MinMaxScaler(feature_range=(-1, 1))
+    price_data = price_data.reshape((-1, 1))
+    price_data_norm = scaler.fit_transform(price_data)
+    price_data = price_data_norm.reshape((-1, 16, 1))
+    print(price_data.shape)
+    price_data = torch.tensor(price_data).float().to(device)
+    with torch.no_grad():
+        output = model(price_data)
+        output = scaler.inverse_transform(output.cpu().numpy())
+    prediction = {
+        "price": output.item(),
+    }
+    return prediction