hechengdong1994's Stars
imputnet/cobalt
save what you love
microsoft/SemanticKernelCookBook
This is a Semantic Kernel's book for beginners
ai-boost/Awesome-GPTs
Curated list of awesome GPTs 👍.
ai-boost/awesome-prompts
Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
weibocom/rill-flow
Rill Flow is a high-performance, scalable workflow orchestration engine for distributed workloads and LLMs
abi/screenshot-to-code
Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)
johnlui/PPHC
📙《高并发的哲学原理》开源图书（CC BY-NC-ND）https://pphc.lvwenhan.com
gavfu/chzhshch-blog
全球第一博客---缠中说禅
webpilot-ai/Webpilot
openai/whisper
Robust Speech Recognition via Large-Scale Weak Supervision
HFTHaidra/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.
nityam007/Stock-Price-Prediction-with-Sentiment-Analysis
More than 90% of traders lose money on stock market because they fail to sync emotions with strategy to trade .Our approach of Stock Price prediction is one the way to solve the problem DJIA index prediction with LSTM-ARIMA hybrid model and News Sentiment Analysis . Achieved accuracy rate of 98.5 % on 75-25 Train Test Split. Combined News + Stock price data is large file size of around 227 MB. If can't download here's link to kaggle :https://www.kaggle.com/aaron7sun/stocknews Access the weights of LSTM ,ARIMA models individually .\ To use ARIMA model - Use command ``` loaded = ARIMAResults.load('arima_model.pkl') ``` \ To use LSTM model - Use command ``` model = tf.keras.models.load_model('lstm_model.h5') ```
JinsenLou/bitcoin_traiding_bot
nge. In this study, the optimization of the three parameters of the Relative Strength Index (RSI) - windowsize, highThreshold, and lowThreshold - was done using a genetic algorithm.The optimized parameters were generated by using training data from a period relatively close in time and applying them to trade in the upcoming duration.
kbmajeed/market_indicators
The objective of this project is to showcase different indicators used in the stock market for trading decisions: Bollinger bands, %b, Relative Strength Index, and Moving Average Convergence Divergence. These help in identifying Bullish and Bearish market, and in deciding when to Short, Long, or Hold a position.
TungPhamDuy/technical-analysis-with-python
The Python project written on Jupyter includes technical analysis using various indicators, developing trading strategies based on the indicators, visualizing the outcomes, and testing the strategies. The indicators applied in the project are Simple Moving Average 200, Bollinger Bands, and RSI - Relative Strength Index.
Gkashish0406/Stock-Pricing
This repository contains various quantitative trading strategies implemented in Python, such as the Black-Scholes model, Relative Strength Index (RSI), Geometric Brownian Motion (GBM), and more. These strategies are used to analyze financial markets and make predictions about future asset prices
yousra-aoudi/Spot-Futures-Arbitrage-Strategy
The repository is a collection of Python classes that implement a trading strategy that aims to profit from discrepancies between spot prices and futures prices of an underlying asset, such as a stock index.
XBT3K/RSI-MA-EURUSD-Algo
This is a modified mean reversion trading strategy that generates buy and sell signals based on the relative strength index (RSI) and moving average (MA) of the EUR/USD currency pair on a 1-minute timeframe.
puneetpushkar/NIFTY50-Backtest-RSI
Backtest of NIFTY50 Index by taking RSI cross-over as a parameter and generate trade logs
jeremyagada/TECHNICAL-TRADING-ANALYSIS-USING-RSI-to
We used the DataReader (yahoo Finance) API, pandas, matplotlib etc to build a technical analysis chart. The technical indicator used is the RSI (Relative Strength Index)
chemicoPy/MACD-RSI-STOCHASTIC-strategy
Trading strategy using indicators - MACD, RSI (Relative Strength Index) & Stochastic oscillator ; taking advantage of a real-time data grabbing from a trusted free leading enterprise sources for mission-critical financial applications through their API
aa3110/python-trading
The trading module is written in python. It's a first concept to figure index, indicator, portfolio and to create signals. The layout is initated with qt5 designer.
chunyip135/stock_signal
A programme that give signal whenever the price index matches the trading strategy.
sherwind/pinescript-mmi_signal
Trend trading strategies filtered by the Market Meanness Index.
SharmaVidhiHaresh/Backtesting-Trading-Strategies-with-Python
In this project, I had backtested the cross-over trading strategy on Google Stock from Jan 2016 to June 2020. By using historical time-series data, I had tested the Moving Average(MA) cross-over strategy and Relative Strength Index (RSI) strategy with a stop loss at a price that closes 2% or more below 10-day MA. I had plotted the equity curve with drawdowns and P&L, as well as volume, relative strength index (RSI), stock pricing chart and simple moving averages.
TalaikisInc/blueblood
Blue Blood Engine - quantitative trading strategies integration and indexing platform.
bideeen/Building-A-Trading-Strategy-With-Python
trading strategy is a fixed plan to go long or short in markets, there are two common trading strategies: the momentum strategy and the reversion strategy. Firstly, the momentum strategy is also called divergence or trend trading. When you follow this strategy, you do so because you believe the movement of a quantity will continue in its current direction. Stated differently, you believe that stocks have momentum or upward or downward trends, that you can detect and exploit. Some examples of this strategy are the moving average crossover, the dual moving average crossover, and turtle trading: The moving average crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market. You’ll see an example of this strategy, which is the “hello world” of quantitative trading later on in this tutorial. The dual moving average crossover occurs when a short-term average crosses a long-term average. This signal is used to identify that momentum is shifting in the direction of the short-term average. A buy signal is generated when the short-term average crosses the long-term average and rises above it, while a sell signal is triggered by a short-term average crossing long-term average and falling below it. Turtle trading is a popular trend following strategy that was initially taught by Richard Dennis. The basic strategy is to buy futures on a 20-day high and sell on a 20-day low. Secondly, the reversion strategy, which is also known as convergence or cycle trading. This strategy departs from the belief that the movement of a quantity will eventually reverse. This might seem a little bit abstract, but will not be so anymore when you take the example. Take a look at the mean reversion strategy, where you actually believe that stocks return to their mean and that you can exploit when it deviates from that mean. That already sounds a whole lot more practical, right? Another example of this strategy, besides the mean reversion strategy, is the pairs trading mean-reversion, which is similar to the mean reversion strategy. Whereas the mean reversion strategy basically stated that stocks return to their mean, the pairs trading strategy extends this and states that if two stocks can be identified that have a relatively high correlation, the change in the difference in price between the two stocks can be used to signal trading events if one of the two moves out of correlation with the other. That means that if the correlation between two stocks has decreased, the stock with the higher price can be considered to be in a short position. It should be sold because the higher-priced stock will return to the mean. The lower-priced stock, on the other hand, will be in a long position because the price will rise as the correlation will return to normal. Besides these two most frequent strategies, there are also other ones that you might come across once in a while, such as the forecasting strategy, which attempts to predict the direction or value of a stock, in this case, in subsequent future time periods based on certain historical factors. There’s also the High-Frequency Trading (HFT) strategy, which exploits the sub-millisecond market microstructure. That’s all music for the future for now; Let’s focus on developing your first trading strategy for now! A Simple Trading Strategy As you read above, you’ll start with the “hello world” of quantitative trading: the moving average crossover. The strategy that you’ll be developing is simple: you create two separate Simple Moving Averages (SMA) of a time series with differing lookback periods, let’s say, 40 days and 100 days. If the short moving average exceeds the long moving average then you go long, if the long moving average exceeds the short moving average then you exit. Remember that when you go long, you think that the stock price will go up and will sell at a higher price in the future (= buy signal); When you go short, you sell your stock, expecting that you can buy it back at a lower price and realize a profit (= sell signal). This simple strategy might seem quite complex when you’re just starting out, but let’s take this step by step: First define your two different lookback periods: a short window and a long window. You set up two variables and assign one integer per variable. Make sure that the integer that you assign to the short window is shorter than the integer that you assign to the long window variable! Next, make an empty signals DataFrame, but do make sure to copy the index of your aapl data so that you can start calculating the daily buy or sell signal for your aapl data. Create a column in your empty signals DataFrame that is named signal and initialize it by setting the value for all rows in this column to 0.0. After the preparatory work, it’s time to create the set of short and long simple moving averages over the respective long and short time windows. Make use of the rolling() function to start your rolling window calculations: within the function, specify the window and the min_period, and set the center argument. In practice, this will result in a rolling() function to which you have passed either short_window or long_window, 1 as the minimum number of observations in the window that are required to have a value, and False, so that the labels are not set at the center of the window. Next, don’t forget to also chain the mean() function so that you calculate the rolling mean. After you have calculated the mean average of the short and long windows, you should create a signal when the short moving average crosses the long moving average, but only for the period greater than the shortest moving average window. In Python, this will result in a condition: signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:]. Note that you add the [short_window:] to comply with the condition “only for the period greater than the shortest moving average window”. When the condition is true, the initialized value 0.0 in the signal column will be overwritten with 1.0. A “signal” is created! If the condition is false, the original value of 0.0 will be kept and no signal is generated. You use the NumPy where() function to set up this condition. Much the same like you read just now, the variable to which you assign this result is signals['signal'][short_window], because you only want to create signals for the period greater than the shortest moving average window! Lastly, you take the difference of the signals in order to generate actual trading orders. In other words, in this column of your signals DataFrame, you’ll be able to distinguish between long and short positions, whether you’re buying or selling stock.
malikfahad/Relative-Strength-Index
The relative strength index (RSI) is a technical indicator used in the analysis of financial markets. It is intended to chart the current and historical strength or weakness of a stock or market based on the closing prices of a recent trading period. The indicator should not be confused with relative strength. The RSI provides signals that tell investors to buy when the currency is oversold and to sell when it is overbought.
futureTechTraders/BasicTradingAlgorithm
Python trading algorithm which suggests entry and exit points based off SimpleMovingAverage, Relative Strength Index, and Moving Average Convergence Divergence. Check out our website: futuretechtraders.org
Stryder-Git/index_calculator
IndexCalculator for trading indices