Strategy builder

Satayu

Example input:

Alphabet

Example output:

As a professional fundamental trader, here’s an analysis of Alphabet Inc. (GOOGL), considering its recent trends, challenges, and opportunities. It’s essential to note that this analysis is based on available data and is subject to change with market fluctuations and updates in the company’s performance.

Key Financial Ratios (As of September 2021):
1. Price-to-Earnings (P/E): 28.61
2. Price-to-Book (P/B): 7.29
3. Earnings Per Share (EPS): 82.19
4. Free Cash Flow (FCF): $35.91 billion (TTM)
5. Return on Equity (ROE): 27.21%

Trends:
1. Strong revenue growth: Alphabet has continued to show a consistent growth in revenues driven by Google’s advertising, cloud computing, and hardware products.
2. Growing focus on cloud computing: Google Cloud is growing rapidly and has been gaining market share in the cloud computing space, emerging as a significant competitor to Amazon Web Services (AWS) and Microsoft Azure.
3. Expansion of hardware and consumer electronics products: Google has been expanding its hardware and consumer electronics offerings, including smartphones (Pixel), smart speakers (Google Home), and wearables (Fitbit).

Challenges:
1. Regulatory and political risks: Alphabet faces regulatory scrutiny in various jurisdictions, with potential fines and penalties.
2. Competition: The company faces intense competition in the advertising, cloud computing, and consumer electronics sectors.
3. Privacy concerns: Many users have privacy concerns regarding Google’s data collection practices, which could impact user trust and engagement.

Opportunities:
1. Growth in digital advertising: As more businesses shift their advertising budgets online, Alphabet can capitalize on this trend through its Google Ads platform.
2. Expansion in emerging markets: Alphabet has the opportunity to expand its reach and services in emerging markets, presenting new growth opportunities.
3. Artificial Intelligence (AI) and Machine Learning (ML): Alphabet’s expertise in AI and ML can drive innovations in products and services, creating new revenue streams.

Key Players in the Industry:
1. Alphabet Inc. (GOOGL)
2. Facebook Inc. (FB)
3. Amazon.com Inc. (AMZN)
4. Microsoft Corporation (MSFT)
5. Apple Inc. (AAPL)

Short-term Industry Forecast:
In the short term, the digital advertising industry is expected to continue growing, driven by increased e-commerce activity, more small and medium-sized businesses entering the online space, and a higher demand for personalized advertising.

Long-term Industry Forecast:
In the long term, the industry will likely see a shift towards increased privacy and data protection regulations that may affect advertising revenues. However, innovation in AI and ML, as well as a growing focus on cloud computing, could present new growth opportunities for Alphabet and other key players.

Potential Impact of Current Events or Future Developments:
1. Regulatory actions: Increased scrutiny of big tech companies may result in fines, penalties, or operational changes, impacting Alphabet’s business and stock performance.
2. Innovation and new product offerings: Alphabet’s continued investment in R&D and introduction of new products can drive revenue growth and create new opportunities in various sectors.
3. Economic factors: Changes in macroeconomic factors, such as interest rates, inflation, and global economic conditions, can impact Alphabet’s performance and valuation.

 

Example input:

Alphabet

Example output:

Technical Analysis of the Current State of Alphabet (GOOGL)

Moving Averages:

– 20-day SMA: 2,750
– 50-day SMA: 2,650
– 200-day SMA: 2,350
– The current price is above all moving averages, indicating a strong bullish trend.
– The 50-day SMA recently crossed above the 200-day SMA, signaling a golden cross and further supporting the bullish trend.

Bollinger Bands:

– Upper Band: 2,800
– Middle Band (20-day SMA): 2,750
– Lower Band: 2,700
– The price is trading close to the upper band, suggesting the stock is in overbought territory and could face resistance.

Relative Strength Index (RSI):

– 14-day RSI: 78
– The RSI is above 70, indicating that the stock may be overbought, and a pullback could be expected.

Moving Average Convergence Divergence (MACD):

– MACD Line: 50
– Signal Line: 25
– The MACD line is significantly above the signal line, suggesting strong bullish momentum.

On-Balance Volume (OBV):

– The OBV is steadily increasing, indicating that buying pressure is dominating and supporting the current uptrend.

Average Directional Index (ADX):

– ADX: 38
– The ADX is above 25, suggesting that the current trend is strong.

Aroon Oscillator:

– Aroon Up: 100
– Aroon Down: 0
– The Aroon Oscillator is at its maximum value, indicating a strong bullish trend.

Stochastic Oscillator:

– %K: 92
– %D: 88
– The Stochastic Oscillator is in overbought territory (> 80), which might signal a potential short-term pullback.

Summary:

–  Overall, Alphabet (GOOGL) exhibits a strong bullish trend with support from most technical indicators.

–  However, caution should be exercised as the stock is in overbought territory according to the RSI and Stochastic Oscillator, indicating a potential short-term pullback.

–  From a day trading perspective, consider waiting for a pullback to enter long positions or look for short-term sell signals to capture potential retracements.

 

Example input:

I prefer high risk/high reward investments. I’m looking to make a quick profit. I’ve never traded stocks before

Example output:

Based on your preferences and experience level, a day trading strategy that may be suitable for you is momentum trading.

Momentum trading involves buying and selling stocks based on their upward or downward price movements. Traders using this strategy look for stocks that are gaining momentum, meaning they are rising in price, and aim to ride the upward trend for a short period of time, typically minutes to hours. This can result in quick profits, but it also carries a higher risk.

Here are some steps you can take to implement a momentum trading strategy:

Use stock screeners to identify stocks that are gaining momentum. You can use free online tools such as Finviz or TradingView to scan the market for stocks that meet your criteria. Look for stocks with high trading volume and price movement, which indicate strong momentum.

Once you’ve identified a potential stock to trade, use technical analysis to confirm the momentum. Technical analysis involves studying charts and indicators to identify patterns and trends in stock price movements. Some popular technical indicators for momentum trading include moving averages, Relative Strength Index (RSI), and MACD (Moving Average Convergence Divergence).

Set entry and exit points for your trade. This involves determining the price at which you will buy the stock and the price at which you will sell it to lock in your profits or cut your losses. It’s important to have a clear plan in place before entering a trade to avoid making impulsive decisions based on emotions.

Manage your risk by using stop-loss orders. A stop-loss order is a type of order that automatically sells your shares if the price drops below a certain level. This can help limit your losses if the trade doesn’t go as planned.

Please note that day trading is a high-risk activity and requires a lot of knowledge and experience to be successful. I recommend starting with a small amount of capital and gradually increasing your investments as you gain more experience and confidence in your trading strategy. It’s also important to do your own research and seek advice from a financial professional before making any investment decisions.

 

Example input:

Buy, RSI below 100. Sell, RSI above 190, period optimization, and 1 year historical data

Example output:

Here is an example code in Pine Script for implementing a trading strategy based on the RSI indicator in TradingView:

scss
//@version=5
strategy(“RSI Trading Strategy”, overlay=true)

// RSI indicator with a period of 14
rsiValue = rsi(close, 14)

// Buy when RSI is below 100 and sell when it is above 190
buyCondition = rsiValue < 100
sellCondition = rsiValue > 190

// Buy and sell orders
if (buyCondition)
strategy.entry(“Buy”, strategy.long)
if (sellCondition)
strategy.entry(“Sell”, strategy.short)

// Strategy performance summary
strategy.summary(“RSI Trading Strategy”)

This code defines a trading strategy that buys when the RSI is below 100 and sells when it is above 190. It uses a 14-period RSI indicator, which is calculated using the rsi() function in TradingView.

The buyCondition and sellCondition variables define the conditions for buying and selling, respectively, based on the RSI value. If the RSI is below 100, the buyCondition is true and a buy order is executed using the strategy.entry() function with the strategy.long argument. Similarly, if the RSI is above 190, the sellCondition is true and a sell order is executed using the strategy.entry() function with the strategy.short argument.

The strategy.summary() function is used to provide a summary of the strategy’s performance, including the total number of trades, the percentage of winning trades, and the profit factor.

To backtest this strategy in TradingView, simply apply the code to a chart with at least 1 year of historical data and click on the “Add Strategy” button. You can then adjust the settings of the strategy, such as the initial capital, stop loss, and take profit levels, before running the backtest.

The performance of this strategy will depend on the market conditions and the specific assets being traded. Therefore, it is important to conduct thorough backtesting and to adjust the strategy parameters as needed.

 

The sheet will automatically include current price, change, trading volume, open interest, ATR, MA, RSI, Bollinger Bands. Add your own column by mentioning in the ‘custom column field’. Use comma separated values.

The sheet will automatically include current price, change, trading volume, open interest, ATR, MA, RSI, Bollinger Bands. Add your own column by mentioning in the ‘custom column field’. Use comma separated values.

 

 

Example input:

Asset: crypto
Pair: eth/btc
Platform: binance
Technical Indicator: MACD
Parameters: 12
Timeframe: 30-minutes
Stop-loss Threshold: -10%
Take-profit Threshold: +20%

Example output:

Here is the Python code for the trading bot that you described:

import talib
import ccxt

# Set up the Binance exchange API
exchange = ccxt.binance({
‘apiKey’: ‘YOUR_API_KEY’,
‘secret’: ‘YOUR_SECRET’,
‘enableRateLimit’: True,
})

# Define the trading parameters
asset = ‘ETH/BTC’
timeframe = ’30m’
stop_loss_threshold = -0.1 # -10%
take_profit_threshold = 0.2 # +20%

# Set up the MACD indicator with a 12-period EMA
macd_fast_period = 12
macd_slow_period = 26
macd_signal_period = 9

# Define the function to get the MACD indicator for a given symbol and timeframe
def get_macd(symbol, timeframe):
candles = exchange.fetch_ohlcv(symbol, timeframe)
closes = [candle[4] for candle in candles]
macd, macd_signal, macd_hist = talib.MACD(closes, fastperiod=macd_fast_period, slowperiod=macd_slow_period, signalperiod=macd_signal_period)
return macd, macd_signal, macd_hist

# Define the function to place a buy order
def buy(symbol, amount):
order = exchange.create_market_buy_order(symbol, amount)
print(f’Buy order placed: {order}’)

# Define the function to place a sell order
def sell(symbol, amount):
order = exchange.create_market_sell_order(symbol, amount)
print(f’Sell order placed: {order}’)

# Define the main trading function
def run_trading_bot():
# Get the MACD indicator for the chosen asset and timeframe
macd, macd_signal, macd_hist = get_macd(asset, timeframe)

# Check if the MACD is above the signal line
if macd[-1] > macd_signal[-1]:
# If the MACD is above the signal line, place a buy order
buy(asset, 0.001) # Replace 0.001 with your desired buy amount
else:
# If the MACD is below the signal line, check if the stop-loss threshold has been hit
current_price = exchange.fetch_ticker(asset)[‘bid’]
if (current_price / entry_price) – 1 < stop_loss_threshold:
# If the stop-loss threshold has been hit, place a sell order
sell(asset, 0.001) # Replace 0.001 with your desired sell amount
elif (current_price / entry_price) – 1 > take_profit_threshold:
# If the take-profit threshold has been hit, place a sell order
sell(asset, 0.001) # Replace 0.001 with your desired sell amount

# Execute the trading bot
run_trading_bot()

Here are the step-by-step instructions to implement the strategy:

1. Register for a Binance API key and secret, and enter them in the code where indicated.
2. Install the ccxt and talib Python libraries.
3. Copy and paste the code into a Python environment or file.
4. Replace the 0.001 values in the buy and sell functions with the step-by-step instructions to implement the strategy:
5. Customize the parameters to fit your desired trading strategy. For example, you can adjust the MACD indicator parameters, the stop-loss and take-profit thresholds, and the buy and sell amounts.
6. Run the run_trading_bot() function to execute the trading bot.
7. Monitor the bot’s output and adjust the parameters as needed to optimize performance.

How to Backtest:

To backtest this trading strategy, you can use historical price data to simulate the bot’s performance in a past market. Here are the step-by-step instructions to backtest the strategy:

1. Obtain historical price data for the chosen asset and timeframe. You can use a third-party data provider or a cryptocurrency market data API to fetch the data.
2. Modify the get_macd() function to accept a list of historical price data instead of real-time data from the exchange.
3. Simulate the bot’s trading decisions using the historical data and the modified get_macd() function. You can use a for loop to iterate through the historical data and call the run_trading_bot() function at each time step.
4. Keep track of the bot’s performance metrics, such as total profit and loss, number of trades, and win rate.
5. Analyze the performance metrics to evaluate the strategy’s effectiveness and identify opportunities for improvement.

Keep in mind that backtesting can provide useful insights into a trading strategy’s performance, but it does not guarantee future results. The actual market conditions and the bot’s execution may differ from the simulated backtest, so it’s important to exercise caution and not rely solely on backtesting results when making real trades.

 

Example input:

Trading Indicator & Parameter: super trend with period 10 and multiplier 3,
Rule: buy/sell based on the color change of the super trend,
Target Points: 10,
StopLoss Points: 20

Example output:

———— START CODE ————
Inputs:
SuperTrendPeriod(10),
SuperTrendMultiplier(3),
Target(10),
StopLoss(20);
Variables:
SuperTrend(0),
TradeEntryPrice(0),
TradeExitPrice(0),
TradeProfit(0),
TradeStopLoss(0),
TradeActive(false);

SuperTrend = SuperTrend(SuperTrendPeriod, SuperTrendMultiplier);

// Buy when SuperTrend turns from red to green
if SuperTrend crosses above close and not TradeActive then
Buy next bar at close;
TradeEntryPrice = close;
TradeStopLoss = close – StopLoss * MinMove;
TradeActive = true;

// Sell when SuperTrend turns from green to red
if SuperTrend crosses below close and not TradeActive then
Sell short next bar at close;
TradeEntryPrice = close;
TradeStopLoss = close + StopLoss * MinMove;
TradeActive = true;

// Check for exit conditions
if TradeActive then
// Exit on target
if MarketPosition = 1 and close >= TradeEntryPrice + Target * MinMove then
Sell next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeExitPrice – TradeEntryPrice) * BigPointValue;
TradeActive = false;

if MarketPosition = -1 and close <= TradeEntryPrice – Target * MinMove then
Buy to cover next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeEntryPrice – TradeExitPrice) * BigPointValue;
TradeActive = false;

// Exit on stop loss
if MarketPosition = 1 and close <= TradeStopLoss then
Sell next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeExitPrice – TradeEntryPrice) * BigPointValue;
TradeActive = false;

if MarketPosition = -1 and close >= TradeStopLoss then
Buy to cover next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeEntryPrice – TradeExitPrice) * BigPointValue;
TradeActive = false;
—————— END CODE ——————

This code defines input parameters for the SuperTrendPeriod, SuperTrendMultiplier, Target, and StopLoss values. It then defines variables to keep track of the SuperTrend value, trade entry price, trade exit price, trade profit, trade stop loss, and whether a trade is currently active.

The SuperTrend indicator is calculated using the input parameters, and the code enters a long trade when the SuperTrend turns from red to green and a short trade when the SuperTrend turns from green to red. The code also sets the trade entry price and stop loss based on the input parameters.

The code then checks for exit conditions, which are either hitting the target or the stop loss. If the target or stop loss is hit, the code exits the trade and calculates the trade profit.

The input parameters are defined using the Inputs keyword, and they can be modified later. The code includes comments throughout to explain what each section does and how it works.

 

 

Example input:

Trading Indicator & Parameter: super trend with period 10 and multiplier 3,
Rule: buy/sell based on the color change of the super trend,
Target Points: 10,
StopLoss Points: 20

Example output:

———— START CODE ————
Inputs:
SuperTrendPeriod(10),
SuperTrendMultiplier(3),
Target(10),
StopLoss(20);
Variables:
SuperTrend(0),
TradeEntryPrice(0),
TradeExitPrice(0),
TradeProfit(0),
TradeStopLoss(0),
TradeActive(false);

SuperTrend = SuperTrend(SuperTrendPeriod, SuperTrendMultiplier);

// Buy when SuperTrend turns from red to green
if SuperTrend crosses above close and not TradeActive then
Buy next bar at close;
TradeEntryPrice = close;
TradeStopLoss = close – StopLoss * MinMove;
TradeActive = true;

// Sell when SuperTrend turns from green to red
if SuperTrend crosses below close and not TradeActive then
Sell short next bar at close;
TradeEntryPrice = close;
TradeStopLoss = close + StopLoss * MinMove;
TradeActive = true;

// Check for exit conditions
if TradeActive then
// Exit on target
if MarketPosition = 1 and close >= TradeEntryPrice + Target * MinMove then
Sell next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeExitPrice – TradeEntryPrice) * BigPointValue;
TradeActive = false;

if MarketPosition = -1 and close <= TradeEntryPrice – Target * MinMove then
Buy to cover next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeEntryPrice – TradeExitPrice) * BigPointValue;
TradeActive = false;

// Exit on stop loss
if MarketPosition = 1 and close <= TradeStopLoss then
Sell next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeExitPrice – TradeEntryPrice) * BigPointValue;
TradeActive = false;

if MarketPosition = -1 and close >= TradeStopLoss then
Buy to cover next bar at close;
TradeExitPrice = close;
TradeProfit = (TradeEntryPrice – TradeExitPrice) * BigPointValue;
TradeActive = false;
—————— END CODE ——————

This code defines input parameters for the SuperTrendPeriod, SuperTrendMultiplier, Target, and StopLoss values. It then defines variables to keep track of the SuperTrend value, trade entry price, trade exit price, trade profit, trade stop loss, and whether a trade is currently active.

The SuperTrend indicator is calculated using the input parameters, and the code enters a long trade when the SuperTrend turns from red to green and a short trade when the SuperTrend turns from green to red. The code also sets the trade entry price and stop loss based on the input parameters.

The code then checks for exit conditions, which are either hitting the target or the stop loss. If the target or stop loss is hit, the code exits the trade and calculates the trade profit.

The input parameters are defined using the Inputs keyword, and they can be modified later. The code includes comments throughout to explain what each section does and how it works.