Merge branch 'master' into complaint

Author: avinashkranjan

AI Driven Storytelling Game/README.md

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+# Interactive Storytelling Game
+
+This is an interactive storytelling game written in Python. In this game, players make choices that influence the storyline and outcome of their adventure.
+
+## Features
+
+- Dynamic storytelling based on player choices.
+- Player can choose character name and traits at the beginning.
+- Random events with effects that modify the story or character traits.
+- Inventory system for collecting items during the adventure.
+- Multiple story nodes with unique scenarios and outcomes.
+- Ending variations based on player decisions.
+
+## Usage
+
+1. Make sure you have Python 3 installed.
+2. Clone or download this repository to your local machine.
+3. Open a terminal or command prompt and navigate to the game directory.
+4. Run the script by executing `python ai-storytelling-game.py`.
+
+## How to Play
+
+1. Enter your character's name when prompted.
+2. Follow the prompts to make choices at various decision points.
+3. Read the story narrative and explore different paths.
+4. Pay attention to your character's traits and inventory items, as they can affect the story.
+5. Reach different endings based on your choices throughout the game.
+
+## Customization
+
+- To create your own story, modify the `nodes_data` list in the `game.py` script. Each node represents a story point with text, options, and optional effects.
+- Customize the effects to add or modify character traits and inventory items.
+- Feel free to expand the game by adding more story nodes, events, and endings.
+
+## Dependencies
+
+This game script uses only Python's standard library and doesn't require any external packages.
+
+## Contributing
+
+If you have any ideas, improvements, or bug fixes, feel free to open an issue or submit a pull request. We appreciate your contributions!

AI Driven Storytelling Game/ai-storytelling-game.py

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+import random
+
+class StoryNode:
+    def __init__(self, text, options=None, effects=None):
+        self.text = text
+        self.options = options or []
+        self.effects = effects or {}
+
+class Character:
+    def __init__(self, name):
+        self.name = name
+        self.inventory = []
+        self.traits = {}
+
+class StoryGame:
+    def __init__(self):
+        self.story_map = {}
+        self.current_node = None
+        self.player = None
+
+    def add_node(self, node_id, text, options=None, effects=None):
+        self.story_map[node_id] = StoryNode(text, options, effects)
+
+    def start(self, node_id):
+        self.current_node = self.story_map.get(node_id)
+
+    def make_choice(self, choice_idx):
+        if self.current_node and choice_idx < len(self.current_node.options):
+            next_node_id = self.current_node.options[choice_idx]
+            self.current_node = self.story_map.get(next_node_id)
+
+    def apply_effects(self, effects):
+        for key, value in effects.items():
+            if key == "add_item":
+                self.player.inventory.append(value)
+            elif key == "add_trait":
+                self.player.traits[value[0]] = value[1]
+
+    def get_current_text(self):
+        text = self.current_node.text if self.current_node else "Game Over"
+        if self.current_node.effects:
+            self.apply_effects(self.current_node.effects)
+        return text.replace("{name}", self.player.name)
+
+    def is_game_over(self):
+        return not self.current_node
+
+# Create the game
+game = StoryGame()
+
+# Create character
+player_name = input("Enter your character's name: ")
+player = Character(player_name)
+game.player = player
+
+nodes_data = [
+    ("start", "You wake up in a mysterious land. Do you explore or rest?", ["explore", "rest"]),
+    ("explore", "You venture deep into the woods and find an old cabin. Do you enter or continue exploring?", ["enter_cabin", "continue_exploring"]),
+    ("rest", "You find a cozy spot and rest for a while. When you wake up, you see a map nearby. Will you follow the map or ignore it?", ["follow_map", "ignore_map"]),
+    ("enter_cabin", "Inside the cabin, you discover a hidden passage. Do you enter the passage or stay in the cabin?", ["enter_passage", "stay_in_cabin"]),
+    ("continue_exploring", "You stumble upon a magical waterfall. Do you drink from it or move on?", ["drink_water", "move_on"]),
+    ("follow_map", "Following the map, you find a hidden treasure. Do you take it or leave it?", ["take_treasure", "leave_treasure"]),
+    ("enter_passage", "The passage leads to an ancient temple. Do you explore further or leave?", ["explore_temple", "leave_temple"]),
+    ("stay_in_cabin", "You find a journal that reveals the history of the land. Do you keep reading or close the journal?", ["keep_reading", "close_journal"]),
+    ("drink_water", "Drinking from the waterfall grants you enhanced senses. Will you use them to uncover secrets or continue your journey?", ["uncover_secrets", "continue_journey"]),
+    ("move_on", "As you move on, you encounter a talking animal. Do you listen to its advice or ignore it?", ["listen_to_animal", "ignore_animal"]),
+    ("enter_passage", "The passage leads to an underground city. Do you explore the city or return to the surface?", ["explore_city", "return_to_surface"]),
+    ("uncover_secrets", "Using your enhanced senses, you find a hidden cave. Do you enter the cave or keep moving?", ["enter_cave", "keep_moving"]),
+    ("listen_to_animal", "The animal warns you of danger ahead. Do you heed its warning or take your chances?", ["heed_warning", "take_chances"]),
+    ("node88", "You come across a portal that leads to another realm. Do you step through or stay?", ["step_through", "stay"]),
+    ("explore_city", "You discover an ancient artifact that can grant a wish. What do you wish for?", ["wish_for_power", "wish_for_wisdom"]),
+    ("return_to_surface", "You emerge from the passage and find yourself in a bustling marketplace. Do you explore or leave?", ["explore_marketplace", "leave_marketplace"]),
+    ("enter_cave", "Inside the cave, you find a friendly spirit. Do you converse with it or leave?", ["converse_with_spirit", "leave_cave"]),
+    ("keep_moving", "You continue your journey and encounter a group of fellow travelers. Do you join them or go your own way?", ["join_travelers", "go_own_way"]),
+    ("heed_warning", "You avoid the danger and find a hidden treasure. What will you do with the treasure?", ["share_treasure", "keep_treasure"]),
+    ("take_chances", "Your gamble pays off, and you uncover a secret passage. Will you enter it or continue?", ["enter_secret_passage", "continue_journey"]),
+    ("step_through", "You step through the portal and find yourself in a futuristic city. How do you navigate this new world?", ["explore_futuristic_city", "find_a_way_back"]),
+    ("stay", "You decide to stay in the current realm, building a new life for yourself.", []),
+    ("explore_marketplace", "In the marketplace, you meet a mysterious merchant. Will you buy a rare item or ignore the merchant?", ["buy_item", "ignore_merchant"]),
+    ("leave_marketplace", "You leave the marketplace and find yourself in a tranquil garden. Do you explore further or rest?", ["explore_garden", "rest_in_garden"]),
+    ("node89", "You face a final challenge. Do you confront it head-on or seek help?", ["confront", "seek_help"]),
+    ("confront", "With courage, you conquer the challenge and become a legend.", []),
+    ("seek_help", "You gather allies and together you overcome the final obstacle.", []),
+]
+
+
+for node_id, text, options, effects in nodes_data:
+    game.add_node(node_id, text, options, effects)
+
+# Start the game
+game.start("start")
+
+# Game loop
+while not game.is_game_over():
+    print(game.get_current_text())
+    if game.current_node.options:
+        choice = input("Enter your choice (0 or 1): ")
+        game.make_choice(int(choice))
+    else:
+        break

AI Driven Storytelling Game/requirements.txt

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+random

Algorithmic Trading Simulation/README.md

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+# Algorithmic Trading Simulation
+
+This Python script simulates an algorithmic trading platform where users can test trading strategies using historical price data. It's designed to provide a basic understanding of how automated trading works. Please note that this is a simplified educational example and not suitable for actual trading.
+
+## Features
+
+- Generates simulated historical price data for testing.
+- Implements a simple moving average strategy for trading signals.
+- Includes risk management based on a specified percentage of portfolio value.
+- Visualizes price data, moving averages, and portfolio value over time.
+
+## Prerequisites
+
+- Python 
+- Required libraries: pandas, numpy, matplotlib
+
+## Usage
+
+1. Ensure you have Python installed.
+2. Install the required libraries: `pip install pandas numpy matplotlib`
+3. Modify the script's parameters, such as strategy settings and risk management parameters.
+4. Run the script: `python algorithmic_trading_simulation.py`
+
+## Disclaimer
+
+This script is meant for educational purposes only and is not intended for actual trading. It lacks important features and considerations necessary for a real-world trading system, such as accurate data, transaction costs, slippage, risk management, and more. Always exercise caution and consult with professionals before engaging in algorithmic trading.
+
+## Contributing
+
+Contributions to the project are welcome! If you have any improvements, bug fixes, or new features to add, feel free to submit a pull request.

Algorithmic Trading Simulation/algorithmic_trading_simulation.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Simulated historical price data (more data points)
+data = {
+    'Date': pd.date_range(start='2010-01-01', periods=1000),
+    'Open': np.random.uniform(100, 200, 1000),
+    'High': np.random.uniform(200, 250, 1000),
+    'Low': np.random.uniform(80, 150, 1000),
+    'Close': np.random.uniform(120, 180, 1000),
+    'Volume': np.random.randint(10000, 50000, 1000)
+}
+
+df = pd.DataFrame(data)
+df.set_index('Date', inplace=True)
+
+# Example trading strategy
+def simple_moving_average_strategy(data, short_window, long_window):
+    signals = pd.DataFrame(index=data.index)
+    signals['Signal'] = 0.0
+    
+    signals['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
+    signals['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1, center=False).mean()
+    
+    signals['Signal'][short_window:] = np.where(signals['Short_MA'][short_window:] > signals['Long_MA'][short_window:], 1.0, 0.0)
+    
+    signals['Positions'] = signals['Signal'].diff()
+    
+    return signals
+
+# Define strategy parameters
+short_window = 10
+long_window = 50
+
+# Apply strategy
+signals = simple_moving_average_strategy(df, short_window, long_window)
+
+# Risk management parameters
+risk_per_trade = 0.02  # 2% risk per trade
+initial_portfolio_value = 100000
+
+# Simulate trading
+position = 0
+portfolio_value = []
+for index, row in signals.iterrows():
+    if row['Positions'] == 1:
+        max_position_size = (risk_per_trade * initial_portfolio_value) / (row['Close'] - row['Low'])
+        position = min(max_position_size, initial_portfolio_value / row['Close'])
+        initial_portfolio_value -= position * row['Close']
+    elif row['Positions'] == -1:
+        initial_portfolio_value += position * row['Close']
+        position = 0
+    portfolio_value.append(initial_portfolio_value + position * row['Close'])
+
+signals['PortfolioValue'] = portfolio_value
+
+# Visualization improvements
+plt.figure(figsize=(12, 8))
+
+plt.subplot(2, 1, 1)
+plt.plot(signals.index, signals['Close'], label='Close Price', alpha=0.5)
+plt.plot(signals.index, signals['Short_MA'], label=f'Short {short_window} MA')
+plt.plot(signals.index, signals['Long_MA'], label=f'Long {long_window} MA')
+plt.legend()
+plt.title('Price Data and Moving Averages')
+
+plt.subplot(2, 1, 2)
+plt.plot(signals.index, signals['PortfolioValue'], label='Portfolio Value', linestyle='--', color='green')
+plt.legend()
+plt.title('Portfolio Value')
+
+plt.tight_layout()
+plt.show()

Algorithmic Trading Simulation/requirements.txt

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+pandas
+numpy
+matplotlib