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Lookahead analysis
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Recursive analysis
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Advanced Strategy
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Advanced Hyperopt
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Orderflow
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Orderflow
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Configure Orderflow Processing
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Accessing Orderflow Data
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Footprint chart (dataframe["orderflow"])
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Orderflow column explanation
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Raw trades data (dataframe["trades"])
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Imbalances (dataframe["imbalances"])
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< h1 id = "orderflow-data" > Orderflow data< a class = "headerlink" href = "#orderflow-data" title = "Permanent link" > ¶ < / a > < / h1 >
< p > This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.< / p >
< div class = "admonition warning" >
< p class = "admonition-title" > Experimental Feature< / p >
< p > The orderflow feature is currently in beta and may be subject to changes in future releases. Please report any issues or feedback on the < a href = "https://github.com/freqtrade/freqtrade/issues" > Freqtrade GitHub repository< / a > .< / p >
< / div >
< div class = "admonition warning" >
< p class = "admonition-title" > Performance< / p >
< p > Orderflow requires raw trades data. This data is rather large, and can cause a slow initial startup, when freqtrade needs to download the trades data for the last X candles. Additionally, enabling this feature will cause increased memory usage. Please ensure to have sufficient resources available.< / p >
< / div >
< h2 id = "getting-started" > Getting Started< a class = "headerlink" href = "#getting-started" title = "Permanent link" > ¶ < / a > < / h2 >
< h3 id = "enable-public-trades" > Enable Public Trades< a class = "headerlink" href = "#enable-public-trades" title = "Permanent link" > ¶ < / a > < / h3 >
< p > In your < code > config.json< / code > file, set the < code > use_public_trades< / code > option to true under the < code > exchange< / code > section.< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "nt" > " exchange" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "p" > {< / span >
< span class = "w" > < / span > < span class = "err" > ...< / span >
< span class = "w" > < / span > < span class = "nt" > " use_public_trades" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "kc" > true< / span > < span class = "p" > ,< / span >
< span class = "p" > }< / span >
< / code > < / pre > < / div >
< h3 id = "configure-orderflow-processing" > Configure Orderflow Processing< a class = "headerlink" href = "#configure-orderflow-processing" title = "Permanent link" > ¶ < / a > < / h3 >
< p > Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:< / p >
< ul >
< li > < code > cache_size< / code > : How many previous orderflow candles are saved into cache instead of calculated every new candle< / li >
< li > < code > max_candles< / code > : Filter how many candles would you like to get trades data for.< / li >
< li > < code > scale< / code > : This controls the price bin size for the footprint chart.< / li >
< li > < code > stacked_imbalance_range< / code > : Defines the minimum consecutive imbalanced price levels required for consideration.< / li >
< li > < code > imbalance_volume< / code > : Filters out imbalances with volume below this threshold.< / li >
< li > < code > imbalance_ratio< / code > : Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.< / li >
< / ul >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "nt" > " orderflow" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "p" > {< / span >
< span class = "w" > < / span > < span class = "nt" > " cache_size" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mi" > 1000< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "nt" > " max_candles" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mi" > 1500< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "nt" > " scale" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mf" > 0.5< / span > < span class = "p" > ,< / span > < span class = "w" > < / span >
< span class = "w" > < / span > < span class = "nt" > " stacked_imbalance_range" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "w" > < / span > < span class = "c1" > // needs at least this amount of imbalance next to each other< / span >
< span class = "w" > < / span > < span class = "nt" > " imbalance_volume" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "w" > < / span > < span class = "c1" > // filters out below< / span >
< span class = "w" > < / span > < span class = "nt" > " imbalance_ratio" < / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "mi" > 3< / span > < span class = "w" > < / span > < span class = "c1" > // filters out ratio lower than< / span >
< span class = "w" > < / span > < span class = "p" > },< / span >
< / code > < / pre > < / div >
< h2 id = "downloading-trade-data-for-backtesting" > Downloading Trade Data for Backtesting< a class = "headerlink" href = "#downloading-trade-data-for-backtesting" title = "Permanent link" > ¶ < / a > < / h2 >
< p > To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.< / p >
< div class = "highlight" > < pre > < span > < / span > < code > freqtrade< span class = "w" > < / span > download-data< span class = "w" > < / span > -p< span class = "w" > < / span > BTC/USDT:USDT< span class = "w" > < / span > --timerange< span class = "w" > < / span > < span class = "m" > 20230101< / span > -< span class = "w" > < / span > --trading-mode< span class = "w" > < / span > futures< span class = "w" > < / span > --timeframes< span class = "w" > < / span > 5m< span class = "w" > < / span > --dl-trades
< / code > < / pre > < / div >
< div class = "admonition warning" >
< p class = "admonition-title" > Data availability< / p >
< p > Not all exchanges provide public trade data. For supported exchanges, freqtrade will warn you if public trade data is not available if you start downloading data with the < code > --dl-trades< / code > flag.< / p >
< / div >
< h2 id = "accessing-orderflow-data" > Accessing Orderflow Data< a class = "headerlink" href = "#accessing-orderflow-data" title = "Permanent link" > ¶ < / a > < / h2 >
< p > Once activated, several new columns become available in your dataframe:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " trades" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Contains information about each individual trade.< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " orderflow" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Represents a footprint chart dict (see below)< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " imbalances" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Contains information about imbalances in the order flow.< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " bid" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Total bid volume < / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " ask" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Total ask volume< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " delta" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Difference between ask and bid volume.< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " min_delta" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Minimum delta within the candle< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " max_delta" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Maximum delta within the candle< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " total_trades" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Total number of trades< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " stacked_imbalances_bid" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Price level of stacked bid imbalance < / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " stacked_imbalances_ask" < / span > < span class = "p" > ]< / span > < span class = "c1" > # Price level of stacked ask imbalance < / span >
< / code > < / pre > < / div >
< p > You can access these columns in your strategy code for further analysis. Here's an example:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > def< / span > < span class = "nf" > populate_indicators< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > dataframe< / span > < span class = "p" > :< / span > < span class = "n" > DataFrame< / span > < span class = "p" > ,< / span > < span class = "n" > metadata< / span > < span class = "p" > :< / span > < span class = "nb" > dict< / span > < span class = "p" > )< / span > < span class = "o" > -> < / span > < span class = "n" > DataFrame< / span > < span class = "p" > :< / span >
< span class = "c1" > # Calculating cumulative delta< / span >
< span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " cum_delta" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > cumulative_delta< / span > < span class = "p" > (< / span > < span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " delta" < / span > < span class = "p" > ])< / span >
< span class = "c1" > # Accessing total trades< / span >
< span class = "n" > total_trades< / span > < span class = "o" > =< / span > < span class = "n" > dataframe< / span > < span class = "p" > [< / span > < span class = "s2" > " total_trades" < / span > < span class = "p" > ]< / span >
< span class = "o" > ...< / span >
< span class = "k" > def< / span > < span class = "nf" > cumulative_delta< / span > < span class = "p" > (< / span > < span class = "n" > delta< / span > < span class = "p" > :< / span > < span class = "n" > Series< / span > < span class = "p" > ):< / span >
< span class = "n" > cumdelta< / span > < span class = "o" > =< / span > < span class = "n" > delta< / span > < span class = "o" > .< / span > < span class = "n" > cumsum< / span > < span class = "p" > ()< / span >
< span class = "k" > return< / span > < span class = "n" > cumdelta< / span >
< / code > < / pre > < / div >
< h3 id = "footprint-chart-dataframeorderflow" > Footprint chart (< code > dataframe["orderflow"]< / code > )< a class = "headerlink" href = "#footprint-chart-dataframeorderflow" title = "Permanent link" > ¶ < / a > < / h3 >
< p > This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The < code > scale< / code > parameter in your configuration determines the price bin size for this representation< / p >
< p > The < code > orderflow< / code > column contains a dict with the following structure:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "go" > {< / span >
< span class = "go" > " price" : {< / span >
< span class = "go" > " bid_amount" : 0.0,< / span >
< span class = "go" > " ask_amount" : 0.0,< / span >
< span class = "go" > " bid" : 0,< / span >
< span class = "go" > " ask" : 0,< / span >
< span class = "go" > " delta" : 0.0,< / span >
< span class = "go" > " total_volume" : 0.0,< / span >
< span class = "go" > " total_trades" : 0< / span >
< span class = "go" > }< / span >
< span class = "go" > }< / span >
< / code > < / pre > < / div >
< h4 id = "orderflow-column-explanation" > Orderflow column explanation< a class = "headerlink" href = "#orderflow-column-explanation" title = "Permanent link" > ¶ < / a > < / h4 >
< ul >
< li > key: Price bin - binned at < code > scale< / code > intervals< / li >
< li > < code > bid_amount< / code > : Total volume bought at each price level.< / li >
< li > < code > ask_amount< / code > : Total volume sold at each price level.< / li >
< li > < code > bid< / code > : Number of buy orders at each price level.< / li >
< li > < code > ask< / code > : Number of sell orders at each price level.< / li >
< li > < code > delta< / code > : Difference between ask and bid volume at each price level.< / li >
< li > < code > total_volume< / code > : Total volume (ask amount + bid amount) at each price level.< / li >
< li > < code > total_trades< / code > : Total number of trades (ask + bid) at each price level.< / li >
< / ul >
< p > By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.< / p >
< h3 id = "raw-trades-data-dataframetrades" > Raw trades data (< code > dataframe["trades"]< / code > )< a class = "headerlink" href = "#raw-trades-data-dataframetrades" title = "Permanent link" > ¶ < / a > < / h3 >
< p > List with the individual trades that occurred during the candle. This data can be used for more granular analysis of order flow dynamics.< / p >
< p > Each individual entry contains a dict with the following keys:< / p >
< ul >
< li > < code > timestamp< / code > : Timestamp of the trade.< / li >
< li > < code > date< / code > : Date of the trade.< / li >
< li > < code > price< / code > : Price of the trade.< / li >
< li > < code > amount< / code > : Volume of the trade.< / li >
< li > < code > side< / code > : Buy or sell.< / li >
< li > < code > id< / code > : Unique identifier for the trade.< / li >
< li > < code > cost< / code > : Total cost of the trade (price * amount).< / li >
< / ul >
< h3 id = "imbalances-dataframeimbalances" > Imbalances (< code > dataframe["imbalances"]< / code > )< a class = "headerlink" href = "#imbalances-dataframeimbalances" title = "Permanent link" > ¶ < / a > < / h3 >
< p > This column provides a dict with information about imbalances in the order flow. An imbalance occurs when there is a significant difference between the ask and bid volume at a given price level.< / p >
< p > Each row looks as follows - with price as index, and the corresponding bid and ask imbalance values as columns< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "go" > {< / span >
< span class = "go" > " price" : {< / span >
< span class = "go" > " bid_imbalance" : False,< / span >
< span class = "go" > " ask_imbalance" : False< / span >
< span class = "go" > }< / span >
< span class = "go" > }< / span >
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